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Labory J, Njomgue-Fotso E, Bottini S. Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data. Comput Struct Biotechnol J 2024; 23:1274-1287. [PMID: 38560281 PMCID: PMC10979063 DOI: 10.1016/j.csbj.2024.03.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/12/2024] [Accepted: 03/18/2024] [Indexed: 04/04/2024] Open
Abstract
Objective Classification tasks are an open challenge in the field of biomedicine. While several machine-learning techniques exist to accomplish this objective, several peculiarities associated with biomedical data, especially when it comes to omics measurements, prevent their use or good performance achievements. Omics approaches aim to understand a complex biological system through systematic analysis of its content at the molecular level. On the other hand, omics data are heterogeneous, sparse and affected by the classical "curse of dimensionality" problem, i.e. having much fewer observation, samples (n) than omics features (p). Furthermore, a major problem with multi-omics data is the imbalance either at the class or feature level. The objective of this work is to study whether feature extraction and/or feature selection techniques can improve the performances of classification machine-learning algorithms on omics measurements. Methods Among all omics, metabolomics has emerged as a powerful tool in cancer research, facilitating a deeper understanding of the complex metabolic landscape associated with tumorigenesis and tumor progression. Thus, we selected three publicly available metabolomics datasets, and we applied several feature extraction techniques both linear and non-linear, coupled or not with feature selection methods, and evaluated the performances regarding patient classification in the different configurations for the three datasets. Results We provide general workflow and guidelines on when to use those techniques depending on the characteristics of the data available. To further test the extension of our approach to other omics data, we have included a transcriptomics and a proteomics data. Overall, for all datasets, we showed that applying supervised feature selection improves the performances of feature extraction methods for classification purposes. Scripts used to perform all analyses are available at: https://github.com/Plant-Net/Metabolomic_project/.
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Affiliation(s)
- Justine Labory
- Université Côte d′Azur, Center of Modeling Simulation and Interactions, Nice, France
- INRAE, Université Côte d′Azur, CNRS, Institut Sophia Agrobiotech, Sophia-Antipolis, France
- Université Côte d′Azur, Inserm U1081, CNRS UMR 7284, Institute for Research on Cancer and Aging, Nice (IRCAN), Nice, France
| | | | - Silvia Bottini
- Université Côte d′Azur, Center of Modeling Simulation and Interactions, Nice, France
- INRAE, Université Côte d′Azur, CNRS, Institut Sophia Agrobiotech, Sophia-Antipolis, France
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Amanollahi M, Jameie M, Looha MA, A Basti F, Cattarinussi G, Moghaddam HS, Di Camillo F, Akhondzadeh S, Pigoni A, Sambataro F, Brambilla P, Delvecchio G. Machine learning applied to the prediction of relapse, hospitalization, and suicide in bipolar disorder using neuroimaging and clinical data: A systematic review. J Affect Disord 2024; 361:778-797. [PMID: 38908556 DOI: 10.1016/j.jad.2024.06.061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 05/22/2024] [Accepted: 06/17/2024] [Indexed: 06/24/2024]
Abstract
BACKGROUND Bipolar disorder (BD) is associated with increased morbidity/mortality. Adverse outcome prediction might help with the management of patients with BD. METHODS We systematically reviewed the performance of machine learning (ML) studies in predicting adverse outcomes (relapse or recurrence, hospital admission, and suicide-related events) in patients with BD. Demographic, clinical, and neuroimaging-related poor outcome predictors were also reviewed. Three databases (PubMed, Scopus, and Web of Science) were explored from inception to July 2023. RESULTS Eighteen studies, accounting for >30,000 patients, were included. Support vector machine, decision trees, random forest, and logistic regression were the most frequently used ML algorithms. ML models' area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity ranged from 0.71 to 0.98, 72.7-92.8 %, and 59.0-95.2 % for relapse/recurrence prediction (4 studies (3 on relapses and 1 on recurrences). The corresponding values were 0.78-0.88, 21.4-100 %, and 77.0-99.7 % for hospital admissions (3 studies, 21,266 patients), and 0.71-0.99, 44.4-97.9 %, and 38.9-95.0 % for suicide-related events (10 studies, 5558 patients). Also, one study addressed a combination of the interest outcomes. Adverse outcome predictors included early onset BD, BD type I, comorbid psychiatric or substance use disorder, circadian rhythm disruption, hospitalization characteristics, and neuroimaging parameters, including increased dynamic amplitude of low-frequency fluctuation, decreased frontolimbic functional connectivity and aberrant dynamic functional connectivity in corticostriatal circuitry. CONCLUSIONS ML models can predict adverse outcomes of BD with relatively acceptable performance measures. Future studies with larger samples and nested cross-validation validation should be conducted to reach more reliable results.
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Affiliation(s)
- Mobina Amanollahi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Melika Jameie
- Neuroscience Research Center, Iran University of Medical Sciences, Tehran, Iran; Iranian Center of Neurological Research, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Azizmohammad Looha
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Fatemeh A Basti
- Islamic Azad University, Tehran Medical Branch, Tehran, Iran
| | - Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, Padua, Italy; Padova Neuroscience Center, University of Padova, Italy
| | - Hossein Sanjari Moghaddam
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran; Psychiatry and Psychology Research Center, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Fabio Di Camillo
- Department of Neuroscience (DNS), University of Padova, Padua, Italy
| | - Shahin Akhondzadeh
- Psychiatry and Psychology Research Center, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Alessandro Pigoni
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padua, Italy; Padova Neuroscience Center, University of Padova, Italy
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
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Chen Z, Liang N, Li H, Zhang H, Li H, Yan L, Hu Z, Chen Y, Zhang Y, Wang Y, Ke D, Shi N. Exploring explainable AI features in the vocal biomarkers of lung disease. Comput Biol Med 2024; 179:108844. [PMID: 38981214 DOI: 10.1016/j.compbiomed.2024.108844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 05/15/2024] [Accepted: 06/04/2024] [Indexed: 07/11/2024]
Abstract
This review delves into the burgeoning field of explainable artificial intelligence (XAI) in the detection and analysis of lung diseases through vocal biomarkers. Lung diseases, often elusive in their early stages, pose a significant public health challenge. Recent advancements in AI have ushered in innovative methods for early detection, yet the black-box nature of many AI models limits their clinical applicability. XAI emerges as a pivotal tool, enhancing transparency and interpretability in AI-driven diagnostics. This review synthesizes current research on the application of XAI in analyzing vocal biomarkers for lung diseases, highlighting how these techniques elucidate the connections between specific vocal features and lung pathology. We critically examine the methodologies employed, the types of lung diseases studied, and the performance of various XAI models. The potential for XAI to aid in early detection, monitor disease progression, and personalize treatment strategies in pulmonary medicine is emphasized. Furthermore, this review identifies current challenges, including data heterogeneity and model generalizability, and proposes future directions for research. By offering a comprehensive analysis of explainable AI features in the context of lung disease detection, this review aims to bridge the gap between advanced computational approaches and clinical practice, paving the way for more transparent, reliable, and effective diagnostic tools.
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Affiliation(s)
- Zhao Chen
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ning Liang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Haoyuan Li
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Haili Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Huizhen Li
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Lijiao Yan
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ziteng Hu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yaxin Chen
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yujing Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yanping Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Dandan Ke
- Special Disease Clinic, Huaishuling Branch of Beijing Fengtai Hospital of Integrated Traditional Chinese and Western Medicine, Beijing, China.
| | - Nannan Shi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
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Dai T, Bao M, Zhang M, Wang Z, Tang J, Liu Z. A risk prediction model based on machine learning algorithm for parastomal hernia after permanent colostomy. BMC Med Inform Decis Mak 2024; 24:224. [PMID: 39118122 PMCID: PMC11308496 DOI: 10.1186/s12911-024-02627-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 08/01/2024] [Indexed: 08/10/2024] Open
Abstract
OBJECTIVE To develop a machine learning-based risk prediction model for postoperative parastomal hernia (PSH) in colorectal cancer patients undergoing permanent colostomy, assisting nurses in identifying high-risk groups and devising preventive care strategies. METHODS A case-control study was conducted on 495 colorectal cancer patients who underwent permanent colostomy at the Second Affiliated Hospital of Anhui Medical University from June 2017 to June 2023, with a 1-year follow-up period. Patients were categorized into PSH and non-PSH groups based on PSH occurrence within 1-year post-operation. Data were split into training (70%) and testing (30%) sets. Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, and binary classification prediction models were established using Logistic Regression (LR), Support Vector Classification (SVC), K Nearest Neighbor (KNN), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Extreme Gradient Boosting (XgBoost). The binary classification label denoted 1 for PSH occurrence and 0 for no PSH occurrence. Parameters were optimized via 5-fold cross-validation. Model performance was evaluated using Area Under Curve (AUC), specificity, sensitivity, accuracy, positive predictive value, negative predictive value, and F1-score. Clinical utility was evaluated using decision curve analysis (DCA), model explanation was enhanced using shapley additive explanation (SHAP), and model visualization was achieved using a nomogram. RESULTS The incidence of PSH within 1 year was 29.1% (144 patients). Among the models tested, the RF model demonstrated the highest discrimination capability with an AUC of 0.888 (95% CI: 0.881-0.935), along with superior specificity, accuracy, sensitivity, and F1 score. It also showed the highest clinical net benefit on the DCA curve. SHAP analysis identified the top 10 influential variables associated with PSH risk: body mass index (BMI), operation duration, history and status of chronic obstructive pulmonary disease (COPD), prealbumin, tumor node metastasis (TNM) staging, stoma site, thickness of rectus abdominis muscle (TRAM), C-reactive protein CRP, american society of anesthesiologists physical status classification (ASA), and stoma diameter. These insights from SHAP plots illustrated how these factors influence individual PSH outcomes. The nomogram was used for model visualization. CONCLUSION The Random Forest model demonstrated robust predictive performance and clinical relevance in forecasting colonic PSH. This model aids in early identification of high-risk patients and guides preventive care.
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Affiliation(s)
- Tian Dai
- Department of General Surgery (Ward one), The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230601, China
- Wound and Stoma Nursing Working Group, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230601, China
| | - Manzhen Bao
- Nursing Department, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230601, China
- Wound and Stoma Nursing Working Group, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230601, China
| | - Miao Zhang
- Nursing Department, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230601, China
| | - Zonggui Wang
- Department of Orthopedics (Ward two), The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230601, China
- Wound and Stoma Nursing Working Group, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230601, China
| | - JingJing Tang
- Department of General Surgery (Ward one), The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230601, China
- Wound and Stoma Nursing Working Group, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230601, China
| | - Zeyan Liu
- Department of Emergency Internal Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230601, China.
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Zan J, Dong X, Yang H, Yan J, He Z, Tian J, Zhang Y. Application of the Unbalanced Ensemble Algorithm for Prognostic Prediction Outcomes of All-Cause Mortality in Coronary Heart Disease Patients Comorbid with Hypertension. Risk Manag Healthc Policy 2024; 17:1921-1936. [PMID: 39135612 PMCID: PMC11317517 DOI: 10.2147/rmhp.s472398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 07/24/2024] [Indexed: 08/15/2024] Open
Abstract
Purpose This study sought to develop an unbalanced-ensemble model that could accurately predict death outcomes of patients with comorbid coronary heart disease (CHD) and hypertension and evaluate the factors contributing to death. Patients and Methods Medical records of 1058 patients with coronary heart disease combined with hypertension and excluding those acute coronary syndrome were collected. Patients were followed-up at the first, third, sixth, and twelfth months after discharge to record death events. Follow-up ended two years after discharge. Patients were divided into survival and nonsurvival groups. According to medical records, gender, smoking, drinking, COPD, cerebral stroke, diabetes, hyperhomocysteinemia, heart failure and renal insufficiency of the two groups were sorted and compared and other influencing factors of the two groups, feature selection was carried out to construct models. Owing to data unbalance, we developed four unbalanced-ensemble prediction models based on Balanced Random Forest (BRF), EasyEnsemble, RUSBoost, SMOTEBoost and the two base classification algorithms based on AdaBoost and Logistic. Each model was optimised using hyperparameters based on GridSearchCV and evaluated using area under the curve (AUC), sensitivity, recall, Brier score, and geometric mean (G-mean). Additionally, to understand the influence of variables on model performance, we constructed a SHapley Additive explanation (SHAP) model based on the optimal model. Results There were significant differences in age, heart rate, COPD, cerebral stroke, heart failure and renal insufficiency in the nonsurvival group compared with the survival group. Among all models, BRF yielded the highest AUC (0.810; 95% CI, 0.778-0.839), sensitivity (0.990; 95% CI, 0.981-1.000), recall (0.990; 95% CI, 0.981-1.000), and G-mean (0.806; 95% CI, 0.778-0.827), and the lowest Brier score (0.181; 95% CI, 0.178-0.185). Therefore, we identified BRF as the optimal model. Furthermore, red blood cell count (RBC), body mass index (BMI), and lactate dehydrogenase were found to be important mortality-associated risk factors. Conclusion BRF combined with advanced machine learning methods and SHAP is highly effective and accurately predicts mortality in patients with CHD comorbid with hypertension. This model has the potential to assist clinicians in modifying treatment strategies to improve patient outcomes.
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Affiliation(s)
- Jiaxin Zan
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, People’s Republic of China
- Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, People’s Republic of China
| | - Xiaojing Dong
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, People’s Republic of China
- Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, People’s Republic of China
| | - Hong Yang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, People’s Republic of China
- Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, People’s Republic of China
| | - Jingjing Yan
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, People’s Republic of China
- Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, People’s Republic of China
| | - Zixuan He
- Department of Cardiology, The First Hospital of Shanxi Medical University, Taiyuan, People’s Republic of China
| | - Jing Tian
- Department of Cardiology, The First Hospital of Shanxi Medical University, Taiyuan, People’s Republic of China
| | - Yanbo Zhang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, People’s Republic of China
- Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, People’s Republic of China
- School of Health Services and Management, Shanxi University of Chinese Medicine, Taiyuan, People’s Republic of China
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Cacciotti A, Pappalettera C, Miraglia F, Carrarini C, Pecchioli C, Rossini PM, Vecchio F. From data to decisions: AI and functional connectivity for diagnosis, prognosis, and recovery prediction in stroke. GeroScience 2024:10.1007/s11357-024-01301-1. [PMID: 39090502 DOI: 10.1007/s11357-024-01301-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 07/23/2024] [Indexed: 08/04/2024] Open
Abstract
Stroke is a severe medical condition which may lead to permanent disability conditions. The initial 8 weeks following a stroke are crucial for rehabilitation, as most recovery occurs during this period. Personalized approaches and predictive biomarkers are needed for tailored rehabilitation. In this context, EEG brain connectivity and Artificial Intelligence (AI) can play a crucial role in diagnosing and predicting stroke outcomes efficiently. In the present study, 127 patients with subacute ischemic lesions and 90 age- and gender-matched healthy controls were enrolled. EEG recordings were obtained from each participant within 15 days of stroke onset. Clinical evaluations were performed at baseline and at 40-days follow-up using the National Institutes of Health Stroke Scale (NIHSS). Functional connectivity analysis was conducted using Total Coherence (TotCoh) and Small Word (SW). Quadratic support vector machines (SVM) algorithms were implemented to classify healthy subjects compared to stroke patients (Healthy vs Stroke), determine the affected hemisphere (Left vs Right Hemisphere), and predict functional recovery (Functional Recovery Prediction). In the classification for Functional Recovery Prediction, an accuracy of 94.75%, sensitivity of 96.27% specificity of 92.33%, and AUC of 0.95 were achieved; for Healthy vs Stroke, an accuracy of 99.09%, sensitivity of 100%, specificity of 98.46%, and AUC of 0.99 were achieved. For Left vs Right Hemisphere classification, accuracy was 86.77%, sensitivity was 91.44%, specificity was 80.33%, and AUC was 0.87. These findings highlight the potential of utilizing functional connectivity measures based on EEG in combination with AI algorithms to improve patient outcomes by targeted rehabilitation interventions.
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Affiliation(s)
- Alessia Cacciotti
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Chiara Pappalettera
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Claudia Carrarini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166, Rome, Italy
- Department of Neuroscience, Catholic University of Sacred Heart, Rome, Italy
| | - Cristiano Pecchioli
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166, Rome, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166, Rome, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166, Rome, Italy.
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy.
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Aghababaei A, Arian R, Soltanipour A, Ashtari F, Rabbani H, Kafieh R. Discrimination of multiple sclerosis using scanning laser ophthalmoscopy images with autoencoder-based feature extraction. Mult Scler Relat Disord 2024; 88:105743. [PMID: 38945032 DOI: 10.1016/j.msard.2024.105743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 06/20/2024] [Indexed: 07/02/2024]
Abstract
OBJECTIVE Optical coherence tomography (OCT) investigations have revealed that the thickness of inner retinal layers becomes decreased in multiple sclerosis (MS) patients, compared to healthy control (HC) individuals. To date, a number of studies have applied machine learning to OCT thickness measurements, aiming to enable accurate and automated diagnosis of the disease. However, there have much less emphasis on other less common retinal imaging modalities, like infrared scanning laser ophthalmoscopy (IR-SLO), for classifying MS. IR-SLO uses laser light to capture high-resolution fundus images, often performed in conjunction with OCT to lock B-scans at a fixed position. METHODS We incorporated two independent datasets of IR-SLO images from the Isfahan and Johns Hopkins centers, consisting of 164 MS and 150 HC images. A subject-wise data splitting approach was employed to ensure that there was no leakage between training and test datasets. Several state-of-the-art convolutional neural networks (CNNs), including VGG-16, VGG-19, ResNet-50, and InceptionV3, and a CNN with a custom architecture were employed. In the next step, we designed a convolutional autoencoder (CAE) to extract semantic features subsequently given as inputs to four conventional ML classifiers, including support vector machine (SVM), k-nearest neighbor (K-NN), random forest (RF), and multi-layer perceptron (MLP). RESULTS The custom CNN (85 % accuracy, 85 % sensitivity, 87 % specificity, 93 % area under the receiver operating characteristics [AUROC], and 94 % area under the precision-recall curve [AUPRC]) outperformed state-of-the-art models (84 % accuracy, 83 % sensitivity, 87 % specificity, 92 % AUROC, and 94 % AUPRC); however, utilizing a combination of the CAE and MLP yields even superior results (88 % accuracy, 86 % sensitivity, 91 % specificity, 94 % AUROC, and 95 % AUPRC). CONCLUSIONS We utilized IR-SLO images to differentiate between MS and HC eyes, with promising results achieved using a combination of CAE and MLP. Future multi-center studies involving more heterogenous data are necessary to assess the feasibility of integrating IR-SLO images into routine clinical practice.
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Affiliation(s)
- Ali Aghababaei
- Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran; School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Roya Arian
- Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Asieh Soltanipour
- Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Fereshteh Ashtari
- Isfahan Neurosciences Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hossein Rabbani
- Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Raheleh Kafieh
- Department of Engineering, Durham University, South Road, Durham, UK.
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Stubberud A, Langseth H, Nachev P, Matharu MS, Tronvik E. Artificial intelligence and headache. Cephalalgia 2024; 44:3331024241268290. [PMID: 39099427 DOI: 10.1177/03331024241268290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/06/2024]
Abstract
BACKGROUND AND METHODS In this narrative review, we introduce key artificial intelligence (AI) and machine learning (ML) concepts, aimed at headache clinicians and researchers. Thereafter, we thoroughly review the use of AI in headache, based on a comprehensive literature search across PubMed, Embase and IEEExplore. Finally, we discuss limitations, as well as ethical and political perspectives. RESULTS We identified six main research topics. First, natural language processing can be used to effectively extract and systematize unstructured headache research data, such as from electronic health records. Second, the most common application of ML is for classification of headache disorders, typically based on clinical record data, or neuroimaging data, with accuracies ranging from around 60% to well over 90%. Third, ML is used for prediction of headache disease trajectories. Fourth, ML shows promise in forecasting of headaches using self-reported data such as triggers and premonitory symptoms, data from wearable sensors and external data. Fifth and sixth, ML can be used for prediction of treatment responses and inference of treatment effects, respectively, aiming to optimize and individualize headache management. CONCLUSIONS The potential uses of AI and ML in headache are broad, but, at present, many studies suffer from poor reporting and lack out-of-sample evaluation, and most models are not validated in a clinical setting.
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Affiliation(s)
- Anker Stubberud
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Neuromedicine and Movement Sciences, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Helge Langseth
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Computer Science, NTNU Norwegian University of Science and Technology, Trondheim, Norway
| | - Parashkev Nachev
- High Dimensional Neurology Group, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Manjit S Matharu
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Headache and Facial Pain Group, UCL Queen Square Institute of Neurology and National Hospital for Neurology and Neurosurgery, London, UK
| | - Erling Tronvik
- NorHead Norwegian Centre for Headache Research, Trondheim, Norway
- Department of Neuromedicine and Movement Sciences, NTNU Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurology and Clinical Neurophysiology, Neuroclinic, StOlav University Hospital, Trondheim, Norway
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Arnold PIM, Janzing JGE, Hommersom A. Machine learning for antidepressant treatment selection in depression. Drug Discov Today 2024; 29:104068. [PMID: 38925472 DOI: 10.1016/j.drudis.2024.104068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 06/07/2024] [Accepted: 06/19/2024] [Indexed: 06/28/2024]
Abstract
Finding the right antidepressant for the individual patient with major depressive disorder can be a difficult endeavor and is mostly based on trial-and-error. Machine learning (ML) is a promising tool to personalize antidepressant prescription. In this review, we summarize the current evidence of ML in the selection of antidepressants and conclude that its value for clinical practice is still limited. Apart from the current focus on effectiveness, several other factors should be taken into account to make ML-based prediction models useful for clinical application.
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Affiliation(s)
- Prehm I M Arnold
- Department of Psychiatry, Radboudumc, Nijmegen, the Netherlands.
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Li Q, Li P, Chen J, Ren R, Ren N, Xia Y. Machine Learning for Predicting Stillbirth: A Systematic Review. Reprod Sci 2024:10.1007/s43032-024-01655-z. [PMID: 39078567 DOI: 10.1007/s43032-024-01655-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 07/11/2024] [Indexed: 07/31/2024]
Abstract
Stillbirth is a major global issue, with over 5 million cases each year. The multifactorial nature of stillbirth makes it difficult to predict. Artificial intelligence (AI) and machine learning (ML) have the potential to enhance clinical decision-making and enable precise assessments. This study reviewed the literature on predictive ML models for stillbirth highlighting input characteristics, performance metrics, and validation. The PubMed, Cochrane, and Web of Science databases were searched for studies using AI to develop predictive models for stillbirth. Findings were analyzed qualitatively using narrative synthesis and graphics. Risk of bias and the applicability of the studies were assessed using PROBAST. Model design and performance were discussed. Eight studies involving 14,840,654 women with gestational ages ranging from 20 weeks to full term were included in the qualitative analysis. Most studies used neural networks, random forests, and logistic regression algorithms. The number of predictive features varied from 14 to 53. Only 50% of studies validated the models. Cross-validation was commonly employed, and only 25% of studies performed external validation. All studies reported area under the curve as a performance metric (range 0.54-0.9), and five studies reported sensitivity (range, 60- 90%) and specificity (range, 64 - 93.3%). A stacked ensemble model that analyzed 53 features performed better than other models (AUC = 0.9; sensitivity and specificity > 85%). Available ML models can attain a considerable degree of accuracy for prediction of stillbirth; however, these models require further development before they can be applied in a clinical setting.
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Affiliation(s)
- Qingyuan Li
- Department of Clinical Medicine, International Medical College of Chongqing Medical University, Yixueyuan Road No.1, Yuzhong District, Chongqing, 400016, China
| | - Pan Li
- Department of Clinical Medicine, Southwest Medical University, Zhongshan Road, No.319 Section 3, Luzhou, 646000, China
| | - Junyu Chen
- School of Public Health, Chongqing Medical University, Yixueyuan Road No.1, Yuzhong District, Chongqing, 400016, China
| | - Ruyu Ren
- School of Public Health, Chongqing Medical University, Yixueyuan Road No.1, Yuzhong District, Chongqing, 400016, China
| | - Ni Ren
- School of Public Health, Chongqing Medical University, Yixueyuan Road No.1, Yuzhong District, Chongqing, 400016, China
| | - Yinyin Xia
- School of Public Health, Chongqing Medical University, Yixueyuan Road No.1, Yuzhong District, Chongqing, 400016, China.
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11
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Cai YQ, Gong DX, Tang LY, Cai Y, Li HJ, Jing TC, Gong M, Hu W, Zhang ZW, Zhang X, Zhang GW. Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions. J Med Internet Res 2024; 26:e47645. [PMID: 38869157 PMCID: PMC11316160 DOI: 10.2196/47645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 10/30/2023] [Accepted: 06/12/2024] [Indexed: 06/14/2024] Open
Abstract
In recent years, there has been explosive development in artificial intelligence (AI), which has been widely applied in the health care field. As a typical AI technology, machine learning models have emerged with great potential in predicting cardiovascular diseases by leveraging large amounts of medical data for training and optimization, which are expected to play a crucial role in reducing the incidence and mortality rates of cardiovascular diseases. Although the field has become a research hot spot, there are still many pitfalls that researchers need to pay close attention to. These pitfalls may affect the predictive performance, credibility, reliability, and reproducibility of the studied models, ultimately reducing the value of the research and affecting the prospects for clinical application. Therefore, identifying and avoiding these pitfalls is a crucial task before implementing the research. However, there is currently a lack of a comprehensive summary on this topic. This viewpoint aims to analyze the existing problems in terms of data quality, data set characteristics, model design, and statistical methods, as well as clinical implications, and provide possible solutions to these problems, such as gathering objective data, improving training, repeating measurements, increasing sample size, preventing overfitting using statistical methods, using specific AI algorithms to address targeted issues, standardizing outcomes and evaluation criteria, and enhancing fairness and replicability, with the goal of offering reference and assistance to researchers, algorithm developers, policy makers, and clinical practitioners.
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Affiliation(s)
- Yu-Qing Cai
- The First Hospital of China Medical University, Shenyang, China
| | - Da-Xin Gong
- Smart Hospital Management Department, The First Hospital of China Medical University, Shenyang, China
| | - Li-Ying Tang
- The First Hospital of China Medical University, Shenyang, China
| | - Yue Cai
- The First Hospital of China Medical University, Shenyang, China
| | - Hui-Jun Li
- Shenyang Medical & Film Science and Technology Co, Ltd, Shenyang, China
| | - Tian-Ci Jing
- Smart Hospital Management Department, The First Hospital of China Medical University, Shenyang, China
| | | | - Wei Hu
- Bayi Orthopedic Hospital, Chengdu, China
| | - Zhen-Wei Zhang
- China Rongtong Medical & Healthcare Co, Ltd, Chengdu, China
| | - Xingang Zhang
- Department of Cardiology, The First Hospital of China Medical University, Shenyang, China
| | - Guang-Wei Zhang
- Smart Hospital Management Department, The First Hospital of China Medical University, Shenyang, China
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12
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Resta E, Resta O, Costantiello A, Leogrande A. The hospital emigration to another region in the light of the environmental, social and governance model in Italy during the period 2004-2021. BMC Public Health 2024; 24:1880. [PMID: 39009998 PMCID: PMC11247882 DOI: 10.1186/s12889-024-19369-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 07/04/2024] [Indexed: 07/17/2024] Open
Abstract
The following article presents an analysis of the impact of the Environmental, Social and Governance-ESG determinants on Hospital Emigration to Another Region-HEAR in the Italian regions in the period 2004-2021. The data are analysed using Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled Ordinary Least Squares-OLS, Weighted Least Squares-WLS, and Dynamic Panel at 1 Stage. Furthermore, to control endogeneity we also created instrumental variable models for each component of the ESG model. Results show that HEAR is negatively associated to the E, S and G component within the ESG model. The data were subjected to clustering with a k-Means algorithm optimized with the Silhouette coefficient. The optimal clustering with k=2 is compared to the sub-optimal cluster with k=3. The results suggest a negative relationship between the resident population and hospital emigration at regional level. Finally, a prediction is proposed with machine learning algorithms classified based on statistical performance. The results show that the Artificial Neural Network-ANN algorithm is the best predictor. The ANN predictions are critically analyzed in light of health economic policy directions.
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Affiliation(s)
| | | | - Alberto Costantiello
- LUM University Giuseppe Degennaro, Strada Statale 100 km 18, Casamassima, Bari, Puglia, Italia
| | - Angelo Leogrande
- LUM University Giuseppe Degennaro, Strada Statale 100 km 18, Casamassima, Bari, Puglia, Italia.
- , LUM Enterprise s.r.l. Strada Statale 100 km 18, Casamassima, Bari, Puglia, Italia.
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13
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Li H, Li F, Wang BS, Zhu BL. Prognostic significance of exportin-5 in hepatocellular carcinoma. World J Gastrointest Oncol 2024; 16:3069-3081. [PMID: 39072169 PMCID: PMC11271777 DOI: 10.4251/wjgo.v16.i7.3069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Revised: 05/05/2024] [Accepted: 05/22/2024] [Indexed: 07/12/2024] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide. As liver cancer often presents no noticeable symptoms in its early stages, most patients are diagnosed at an advanced stage, complicating treatment. Therefore, the identification of new biomarkers is crucial for the early detection and treatment of HCC. Research on exportin-5 (XPO5) could offer new avenues for early diagnosis and improve treatment strategies. AIM To explore the role of XPO5 in HCC progression and its potential as a prognostic biomarker. METHODS This study assessed XPO5 mRNA expression in HCC using The Cancer Genome Atlas, TIMER, and International Cancer Genome Consortium databases, correlating it with clinical profiles and disease progression. We performed in vitro experiments to examine the effect of XPO5 on liver cell growth. Gene Set Enrichment Analysis, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology were used to elucidate the biological roles and signaling pathways. We also evaluated XPO5's impact on immune cell infiltration and validated its prognostic potential using machine learning. RESULTS XPO5 was significantly upregulated in HCC tissues, correlating with tumor grade, T-stage, and overall survival, indicating poor prognosis. Enrichment analyses linked high XPO5 expression with tumor immunity, particularly CD4 T cell memory activation and macrophage M0 infiltration. Drug sensitivity tests identified potential therapeutic agents such as MG-132, paclitaxel, and WH-4-023. Overexpression of XPO5 in HCC cells, compared to normal liver cells, was confirmed by western blotting and quantitative real-time polymerase chain reaction. The lentiviral transduction-mediated knockdown of XPO5 significantly reduced cell proliferation and metastasis. Among the various machine learning algorithms, the C5.0 decision tree algorithm achieved accuracy rates of 95.5% in the training set and 92.0% in the validation set. CONCLUSION Our analysis shows that XPO5 expression is a reliable prognostic indicator for patients with HCC and is significantly associated with immune cell infiltration.
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Affiliation(s)
- Hao Li
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education, Southeast University, Nanjing 210000, Jiangsu Province, China
- Institute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210000, Jiangsu Province, China
| | - Fei Li
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education, Southeast University, Nanjing 210000, Jiangsu Province, China
- Institute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210000, Jiangsu Province, China
| | - Bo-Shen Wang
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education, Southeast University, Nanjing 210000, Jiangsu Province, China
- Institute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210000, Jiangsu Province, China
| | - Bao-Li Zhu
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education, Southeast University, Nanjing 210000, Jiangsu Province, China
- Institute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210000, Jiangsu Province, China
- Committee, Jiangsu Preventive Medical Association, Nanjing 210000, Jiangsu Province, China
- Center for Global Health, Nanjing Medical University, Nanjing 210000, Jiangsu Province, China
- Public Health Sector, Jiangsu Province Engineering Research Center Jiangsu Province Engineering Research Center of Health Emergency, Nanjing 210000, Jiangsu Province, China
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14
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Gurnani B, Kaur K, Lalgudi VG, Kundu G, Mimouni M, Liu H, Jhanji V, Prakash G, Roy AS, Shetty R, Gurav JS. Role of artificial intelligence, machine learning and deep learning models in corneal disorders - A narrative review. J Fr Ophtalmol 2024; 47:104242. [PMID: 39013268 DOI: 10.1016/j.jfo.2024.104242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 07/18/2024]
Abstract
In the last decade, artificial intelligence (AI) has significantly impacted ophthalmology, particularly in managing corneal diseases, a major reversible cause of blindness. This review explores AI's transformative role in the corneal subspecialty, which has adopted advanced technology for superior clinical judgment, early diagnosis, and personalized therapy. While AI's role in anterior segment diseases is less documented compared to glaucoma and retinal pathologies, this review highlights its integration into corneal diagnostics through imaging techniques like slit-lamp biomicroscopy, anterior segment optical coherence tomography (AS-OCT), and in vivo confocal biomicroscopy. AI has been pivotal in refining decision-making and prognosis for conditions such as keratoconus, infectious keratitis, and dystrophies. Multi-disease deep learning neural networks (MDDNs) have shown diagnostic ability in classifying corneal diseases using AS-OCT images, achieving notable metrics like an AUC of 0.910. AI's progress over two decades has significantly improved the accuracy of diagnosing conditions like keratoconus and microbial keratitis. For instance, AI has achieved a 90.7% accuracy rate in classifying bacterial and fungal keratitis and an AUC of 0.910 in differentiating various corneal diseases. Convolutional neural networks (CNNs) have enhanced the analysis of color-coded corneal maps, yielding up to 99.3% diagnostic accuracy for keratoconus. Deep learning algorithms have also shown robust performance in detecting fungal hyphae on in vivo confocal microscopy, with precise quantification of hyphal density. AI models combining tomography scans and visual acuity have demonstrated up to 97% accuracy in keratoconus staging according to the Amsler-Krumeich classification. However, the review acknowledges the limitations of current AI models, including their reliance on binary classification, which may not capture the complexity of real-world clinical presentations with multiple coexisting disorders. Challenges also include dependency on data quality, diverse imaging protocols, and integrating multimodal images for a generalized AI diagnosis. The need for interpretability in AI models is emphasized to foster trust and applicability in clinical settings. Looking ahead, AI has the potential to unravel the intricate mechanisms behind corneal pathologies, reduce healthcare's carbon footprint, and revolutionize diagnostic and management paradigms. Ethical and regulatory considerations will accompany AI's clinical adoption, marking an era where AI not only assists but augments ophthalmic care.
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Affiliation(s)
- B Gurnani
- Department of Cataract, Cornea, External Disease, Trauma, Ocular Surface and Refractive Surgery, ASG Eye Hospital, Jodhpur, Rajasthan, India.
| | - K Kaur
- Department of Cataract, Pediatric Ophthalmology and Strabismus, ASG Eye Hospital, Jodhpur, Rajasthan, India
| | - V G Lalgudi
- Department of Cornea, Refractive surgery, Ira G Ross Eye Institute, Jacobs School of Medicine and Biomedical Sciences, State University of New York (SUNY), Buffalo, USA
| | - G Kundu
- Department of Cornea and Refractive Surgery, Narayana Nethralaya, Bangalore, India
| | - M Mimouni
- Department of Ophthalmology, Rambam Health Care Campus affiliated with the Bruce and Ruth Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | - H Liu
- Department of Ophthalmology, University of Ottawa Eye Institute, Ottawa, Canada
| | - V Jhanji
- UPMC Eye Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - G Prakash
- Department of Ophthalmology, School of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - A S Roy
- Narayana Nethralaya Foundation, Bangalore, India
| | - R Shetty
- Department of Cornea and Refractive Surgery, Narayana Nethralaya, Bangalore, India
| | - J S Gurav
- Department of Opthalmology, Armed Forces Medical College, Pune, India
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15
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Ang CYS, Chiew YS, Wang X, Ooi EH, Cove ME, Chen Y, Zhou C, Chase JG. Patient-ventilator asynchrony classification in mechanically ventilated patients: Model-based or machine learning method? COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 255:108323. [PMID: 39029417 DOI: 10.1016/j.cmpb.2024.108323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 06/27/2024] [Accepted: 07/10/2024] [Indexed: 07/21/2024]
Abstract
BACKGROUND AND OBJECTIVE Patient-ventilator asynchrony (PVA) is associated with poor clinical outcomes and remains under-monitored. Automated PVA detection would enable complete monitoring standard observational methods do not allow. While model-based and machine learning PVA approaches exist, they have variable performance and can miss specific PVA events. This study compares a model and rule-based algorithm with a machine learning PVA method by retrospectively validating both methods using an independent patient cohort. METHODS Hysteresis loop analysis (HLA) which is a rule-based method (RBM) and a tri-input convolutional neural network (TCNN) machine learning model are used to classify 7 different types of PVA, including: 1) flow asynchrony; 2) reverse triggering; 3) premature cycling; 4) double triggering; 5) delayed cycling; 6) ineffective efforts; and 7) auto triggering. Class activation mapping (CAM) heatmaps visualise sections of respiratory waveforms the TCNN model uses for decision making, improving result interpretability. Both PVA classification methods were used to classify incidence in an independent retrospective clinical cohort of 11 mechanically ventilated patients for validation and performance comparison. RESULTS Self-validation with the training dataset shows overall better HLA performance (accuracy, sensitivity, specificity: 97.5 %, 96.6 %, 98.1 %) compared to the TCNN model (accuracy, sensitivity, specificity: 89.5 %, 98.3 %, 83.9 %). In this study, the TCNN model demonstrates higher sensitivity in detecting PVA, but HLA was better at identifying non-PVA breathing cycles due to its rule-based nature. While the overall AI identified by both classification methods are very similar, the intra-patient distribution of each PVA type varies between HLA and TCNN. CONCLUSION The collective findings underscore the efficacy of both HLA and TCNN in PVA detection, indicating the potential for real-time continuous monitoring of PVA. While ML methods such as TCNN demonstrate good PVA identification performance, it is essential to ensure optimal model architecture and diversity in training data before widespread uptake as standard care. Moving forward, further validation and adoption of RBM methods, such as HLA, offers an effective approach to PVA detection while providing clear distinction into the underlying patterns of PVA, better aligning with clinical needs for transparency, explicability, adaptability and reliability of these emerging tools for clinical care.
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Affiliation(s)
| | - Yeong Shiong Chiew
- School of Engineering, Monash University Malaysia, Selangor, Malaysia; Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand.
| | - Xin Wang
- School of Engineering, Monash University Malaysia, Selangor, Malaysia
| | - Ean Hin Ooi
- School of Engineering, Monash University Malaysia, Selangor, Malaysia
| | - Matthew E Cove
- Division of Respiratory & Critical Care Medicine, Department of Medicine, National University Health System, Singapore
| | - Yuhong Chen
- Intensive Care Unit, the Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, China
| | - Cong Zhou
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
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16
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Hsia JY, Chang CC, Liu CF, Chou CL, Yang CC. Longitudinal Risk Analysis of Second Primary Cancer after Curative Treatment in Patients with Rectal Cancer. Diagnostics (Basel) 2024; 14:1461. [PMID: 39001350 PMCID: PMC11241612 DOI: 10.3390/diagnostics14131461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Accepted: 07/04/2024] [Indexed: 07/16/2024] Open
Abstract
Predicting and improving the response of rectal cancer to second primary cancers (SPCs) remains an active and challenging field of clinical research. Identifying predictive risk factors for SPCs will help guide more personalized treatment strategies. In this study, we propose that experience data be used as evidence to support patient-oriented decision-making. The proposed model consists of two main components: a pipeline for extraction and classification and a clinical risk assessment. The study includes 4402 patient datasets, including 395 SPC patients, collected from three cancer registry databases at three medical centers; based on literature reviews and discussion with clinical experts, 10 predictive variables were considered risk factors for SPCs. The proposed extraction and classification pipelines that classified patients according to importance were age at diagnosis, chemotherapy, smoking behavior, combined stage group, and sex, as has been proven in previous studies. The C5 method had the highest predicted AUC (84.88%). In addition, the proposed model was associated with a classification pipeline that showed an acceptable testing accuracy of 80.85%, a recall of 79.97%, a specificity of 88.12%, a precision of 85.79%, and an F1 score of 79.88%. Our results indicate that chemotherapy is the most important prognostic risk factor for SPCs in rectal cancer survivors. Furthermore, our decision tree for clinical risk assessment illuminates the possibility of assessing the effectiveness of a combination of these risk factors. This proposed model may provide an essential evaluation and longitudinal change for personalized treatment of rectal cancer survivors in the future.
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Affiliation(s)
- Jiun-Yi Hsia
- Division of Thoracic Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung 402367, Taiwan;
- School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
| | - Chi-Chang Chang
- School of Medical Informatics, Chung Shan Medical University, IT Office, Chung Shan Medical University Hospital, Taichung 40201, Taiwan;
- Department of Information Management, Ming Chuan University, Taoyuan 33348, Taiwan
| | - Chung-Feng Liu
- Department of Medical Research, Chi Mei Medical Center, Tainan 710402, Taiwan;
| | - Chia-Lin Chou
- Division of Colon & Rectal Surgery, Department of Surgery, Chi Mei Medical Center, Tainan 710402, Taiwan
- Department of Medical Laboratory Science and Biotechnology, Chung Hwa University of Medical Technology, Tainan 71703, Taiwan
| | - Ching-Chieh Yang
- Department of Radiation Oncology, Chi Mei Medical Center, Tainan 71004, Taiwan
- Department of Pharmacy, Chia-Nan University of Pharmacy and Science, Tainan 717301, Taiwan
- School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung 80404, Taiwan
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17
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Hutchinson JM, Raffoul A, Pepetone A, Andrade L, Williams TE, McNaughton SA, Leech RM, Reedy J, Shams-White MM, Vena JE, Dodd KW, Bodnar LM, Lamarche B, Wallace MP, Deitchler M, Hussain S, Kirkpatrick SI. Advances in methods for characterizing dietary patterns: A scoping review. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.20.24309251. [PMID: 38947003 PMCID: PMC11213084 DOI: 10.1101/2024.06.20.24309251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
There is a growing focus on better understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterize dietary patterns, such as machine learning algorithms and latent class analysis methods, may offer opportunities to measure and characterize dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterize dietary patterns. This scoping review synthesized literature from 2005-2022 applying methods not traditionally used to characterize dietary patterns, referred to as novel methods. MEDLINE, CINAHL, and Scopus were searched using keywords including machine learning, latent class analysis, and least absolute shrinkage and selection operator (LASSO). Of 5274 records identified, 24 met the inclusion criteria. Twelve of 24 articles were published since 2020. Studies were conducted across 17 countries. Nine studies used approaches that have applications in machine learning to identify dietary patterns. Fourteen studies assessed associations between dietary patterns that were characterized using novel methods and health outcomes, including cancer, cardiovascular disease, and asthma. There was wide variation in the methods applied to characterize dietary patterns and in how these methods were described. The extension of reporting guidelines and quality appraisal tools relevant to nutrition research to consider specific features of novel methods may facilitate complete and consistent reporting and enable evidence synthesis to inform policies and programs aimed at supporting healthy dietary patterns.
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Affiliation(s)
- Joy M Hutchinson
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Amanda Raffoul
- Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada
| | - Alexandra Pepetone
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Lesley Andrade
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Tabitha E Williams
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Sarah A McNaughton
- Health and Well-Being Centre for Research Innovation, School of Human Movement and Nutrition Sciences, University of Queensland, St. Lucia, QLD, Australia
| | - Rebecca M Leech
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Victoria, Geelong, Australia
| | - Jill Reedy
- National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Marissa M Shams-White
- Population Science Department, American Cancer Society, Washington DC, USA
- Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA
| | - Jennifer E Vena
- Alberta's Tomorrow Project, Alberta Health Services, Edmonton, AB, Canada
| | - Kevin W Dodd
- Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA
| | - Lisa M Bodnar
- School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Benoît Lamarche
- Centre Nutrition, santé et société (NUTRISS), Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec City, QC, Canada
| | - Michael P Wallace
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada
| | - Megan Deitchler
- Intake - Center for Dietary Assessment, FHI Solutions, Washington, DC, USA
| | - Sanaa Hussain
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
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18
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Yan L, Su H, Liu J, Wen X, Luo H, Yin Y, Guo X. Rapid detection of lung cancer based on serum Raman spectroscopy and a support vector machine: a case-control study. BMC Cancer 2024; 24:791. [PMID: 38956551 PMCID: PMC11220989 DOI: 10.1186/s12885-024-12578-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 06/28/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND Early screening and detection of lung cancer is essential for the diagnosis and prognosis of the disease. In this paper, we investigated the feasibility of serum Raman spectroscopy for rapid lung cancer screening. METHODS Raman spectra were collected from 45 patients with lung cancer, 45 with benign lung lesions, and 45 healthy volunteers. And then the support vector machine (SVM) algorithm was applied to build a diagnostic model for lung cancer. Furthermore, 15 independent individuals were sampled for external validation, including 5 lung cancer patients, 5 benign lung lesion patients, and 5 healthy controls. RESULTS The diagnostic sensitivity, specificity, and accuracy were 91.67%, 92.22%, 90.56% (lung cancer vs. healthy control), 92.22%,95.56%,93.33% (benign lung lesion vs. healthy) and 80.00%, 83.33%, 80.83% (lung cancer vs. benign lung lesion), repectively. In the independent validation cohort, our model showed that all the samples were classified correctly. CONCLUSION Therefore, this study demonstrates that the serum Raman spectroscopy analysis technique combined with the SVM algorithm has great potential for the noninvasive detection of lung cancer.
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Affiliation(s)
- Linfang Yan
- Guang'an People's Hospital, Guang'an, Sichuan Province, China
| | - Huiting Su
- Guang'an People's Hospital, Guang'an, Sichuan Province, China.
| | - Jiafei Liu
- Guang'an People's Hospital, Guang'an, Sichuan Province, China
| | - Xiaozheng Wen
- Guang'an People's Hospital, Guang'an, Sichuan Province, China
| | - Huaichao Luo
- Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, China
| | - Yu Yin
- Sichuan Institute for Brain Science and Brain-Inspired Intelligence, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoqiang Guo
- Guang'an People's Hospital, Guang'an, Sichuan Province, China
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19
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Adams LC, Bressem KK, Poddubnyy D. Artificial intelligence and machine learning in axial spondyloarthritis. Curr Opin Rheumatol 2024; 36:267-273. [PMID: 38533807 DOI: 10.1097/bor.0000000000001015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
PURPOSE OF REVIEW To evaluate the current applications and prospects of artificial intelligence and machine learning in diagnosing and managing axial spondyloarthritis (axSpA), focusing on their role in medical imaging, predictive modelling, and patient monitoring. RECENT FINDINGS Artificial intelligence, particularly deep learning, is showing promise in diagnosing axSpA assisting with X-ray, computed tomography (CT) and MRI analyses, with some models matching or outperforming radiologists in detecting sacroiliitis and markers. Moreover, it is increasingly being used in predictive modelling of disease progression and personalized treatment, and could aid risk assessment, treatment response and clinical subtype identification. Variable study designs, sample sizes and the predominance of retrospective, single-centre studies still limit the generalizability of results. SUMMARY Artificial intelligence technologies have significant potential to advance the diagnosis and treatment of axSpA, providing more accurate, efficient and personalized healthcare solutions. However, their integration into clinical practice requires rigorous validation, ethical and legal considerations, and comprehensive training for healthcare professionals. Future advances in artificial intelligence could complement clinical expertise and improve patient care through improved diagnostic accuracy and tailored therapeutic strategies, but the challenge remains to ensure that these technologies are validated in prospective multicentre trials and ethically integrated into patient care.
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Affiliation(s)
- Lisa C Adams
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine
| | - Keno K Bressem
- Institute for Radiology and Nuclear Medicine, German Heart Centre Munich, Technical University of Munich, Munich
| | - Denis Poddubnyy
- Department of Gastroenterology, Infectiology and Rheumatology (including Nutrition Medicine), Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin
- Epidemiology Unit, German Rheumatism Research Centre, Berlin, Germany
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Fung A, Loutet M, Roth DE, Wong E, Gill PJ, Morris SK, Beyene J. Clinical prediction models in children that use repeated measurements with time-varying covariates: a scoping review. Acad Pediatr 2024; 24:728-740. [PMID: 38561061 DOI: 10.1016/j.acap.2024.03.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 02/29/2024] [Accepted: 03/27/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND Emerging evidence suggests that clinical prediction models that use repeated (time-varying) measurements within each patient may have higher predictive accuracy than models that use patient information from a single measurement. OBJECTIVE To determine the breadth of the published literature reporting the development of clinical prediction models in children that use time-varying predictors. DATA SOURCES MEDLINE, EMBASE and Cochrane databases. ELIGIBILITY CRITERIA We included studies reporting the development of a multivariable clinical prediction model in children, with or without validation, to predict a repeatedly measured binary or time-to-event outcome and utilizing at least one repeatedly measured predictor. SYNTHESIS METHODS We categorized included studies by the method used to model time-varying predictors. RESULTS Of 99 clinical prediction model studies that had a repeated measurements data structure, only 27 (27%) used methods that incorporated the repeated measurements as time-varying predictors in a single model. Among these 27 time-varying prediction model studies, we grouped model types into nine categories: time-dependent Cox regression, generalized estimating equations, random effects model, landmark model, joint model, neural network, K-nearest neighbor, support vector machine and tree-based algorithms. Where there was comparison of time-varying models to single measurement models, using time-varying predictors improved predictive accuracy. CONCLUSIONS Various methods have been used to develop time-varying prediction models in children, but there is a paucity of pediatric time-varying models in the literature. Incorporating time-varying covariates in pediatric prediction models may improve predictive accuracy. Future research in pediatric prediction model development should further investigate whether incorporation of time-varying covariates improves predictive accuracy.
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Affiliation(s)
- Alastair Fung
- Division of Paediatric Medicine (A Fung, DE Roth, and PJ Gill), Hospital for Sick Children, Toronto, Ontario, Canada; Dalla Lana School of Public Health (A Fung, M Loutet, DE Roth, PJ Gill, SK Morris, and J Beyene), University of Toronto, Toronto, Ontario, Canada; Centre for Global Child Health (A Fung, M Loutet, DE Roth, and SK Morris), Hospital for Sick Children, Toronto, Ontario, Canada.
| | - Miranda Loutet
- Dalla Lana School of Public Health (A Fung, M Loutet, DE Roth, PJ Gill, SK Morris, and J Beyene), University of Toronto, Toronto, Ontario, Canada; Centre for Global Child Health (A Fung, M Loutet, DE Roth, and SK Morris), Hospital for Sick Children, Toronto, Ontario, Canada
| | - Daniel E Roth
- Division of Paediatric Medicine (A Fung, DE Roth, and PJ Gill), Hospital for Sick Children, Toronto, Ontario, Canada; Dalla Lana School of Public Health (A Fung, M Loutet, DE Roth, PJ Gill, SK Morris, and J Beyene), University of Toronto, Toronto, Ontario, Canada; Centre for Global Child Health (A Fung, M Loutet, DE Roth, and SK Morris), Hospital for Sick Children, Toronto, Ontario, Canada; Temerty Faculty of Medicine (DE Roth, E Wong, PJ Gill, and SK Morris), University of Toronto, Toronto, Ontario, Canada; Child Health Evaluative Sciences (DE Roth, PJ Gill, and SK Morris), Hospital for Sick Children Research Institute, Toronto, Ontario, Canada
| | - Elliott Wong
- Temerty Faculty of Medicine (DE Roth, E Wong, PJ Gill, and SK Morris), University of Toronto, Toronto, Ontario, Canada
| | - Peter J Gill
- Division of Paediatric Medicine (A Fung, DE Roth, and PJ Gill), Hospital for Sick Children, Toronto, Ontario, Canada; Dalla Lana School of Public Health (A Fung, M Loutet, DE Roth, PJ Gill, SK Morris, and J Beyene), University of Toronto, Toronto, Ontario, Canada; Temerty Faculty of Medicine (DE Roth, E Wong, PJ Gill, and SK Morris), University of Toronto, Toronto, Ontario, Canada; Child Health Evaluative Sciences (DE Roth, PJ Gill, and SK Morris), Hospital for Sick Children Research Institute, Toronto, Ontario, Canada
| | - Shaun K Morris
- Dalla Lana School of Public Health (A Fung, M Loutet, DE Roth, PJ Gill, SK Morris, and J Beyene), University of Toronto, Toronto, Ontario, Canada; Centre for Global Child Health (A Fung, M Loutet, DE Roth, and SK Morris), Hospital for Sick Children, Toronto, Ontario, Canada; Temerty Faculty of Medicine (DE Roth, E Wong, PJ Gill, and SK Morris), University of Toronto, Toronto, Ontario, Canada; Child Health Evaluative Sciences (DE Roth, PJ Gill, and SK Morris), Hospital for Sick Children Research Institute, Toronto, Ontario, Canada; Division of Infectious Diseases (SK Morris), Hospital for Sick Children, Toronto, Ontario, Canada
| | - Joseph Beyene
- Dalla Lana School of Public Health (A Fung, M Loutet, DE Roth, PJ Gill, SK Morris, and J Beyene), University of Toronto, Toronto, Ontario, Canada; Department of Health Research Methods, Evidence and Impact (J Beyene), Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
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Lebech Cichosz S, Hasselstrøm Jensen M, Schou Olesen S. Development and Validation of a Machine Learning Model to Predict Weekly Risk of Hypoglycemia in Patients with Type 1 Diabetes Based on Continuous Glucose Monitoring. Diabetes Technol Ther 2024; 26:457-466. [PMID: 38215207 DOI: 10.1089/dia.2023.0532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2024]
Abstract
Aim: The aim of this study was to develop and validate a prediction model based on continuous glucose monitoring (CGM) data to identify a week-to-week risk profile of excessive hypoglycemia. Methods: We analyzed, trained, and internally tested two prediction models using CGM data from 205 type 1 diabetes patients with long-term CGM monitoring. A binary classification approach (XGBoost) combined with feature engineering deployed on the CGM signals was utilized to predict excessive hypoglycemia risk defined by two targets (time below range [TBR] >4% and the upper TBR 90th percentile limit) of TBR the following week. The models were validated in two independent cohorts with a total of 253 additional patients. Results: A total of 61,470 weeks of CGM data were included in the analysis. The XGBoost models had an area under the receiver operating characteristic curve (ROC-AUC) of 0.83-0.87 (95% confidence interval; 0.83-0.88) in the test dataset. The external validation showed ROC-AUCs of 0.81-0.90. The most discriminative features included the low blood glucose index, the glycemic risk assessment diabetes equation (GRADE), hypoglycemia, the TBR, waveform length, the coefficient of variation and mean glucose during the previous week. This highlights that the pattern of hypoglycemia combined with glucose variability during the past week contains information on the risk of future hypoglycemia. Conclusion: Prediction models based on real-world CGM data can be used to predict the risk of hypoglycemia in the forthcoming week. The models showed good performance in both the internal and external validation cohorts.
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Affiliation(s)
- Simon Lebech Cichosz
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | | | - Søren Schou Olesen
- Department of Clinical Medicine, Aalborg University Hospital, Aalborg, Denmark
- Department of Gastroenterology and Hepatology, Centre for Pancreatic Diseases and Mech-Sense, Aalborg University Hospital, Aalborg, Denmark
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22
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Nopour R. Establishment of prediction model for mortality risk of pancreatic cancer: a retrospective study. BMC Med Inform Decis Mak 2024; 24:181. [PMID: 38937795 PMCID: PMC11210158 DOI: 10.1186/s12911-024-02590-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 06/25/2024] [Indexed: 06/29/2024] Open
Abstract
BACKGROUND AND AIM Pancreatic cancer possesses a high prevalence and mortality rate among other cancers. Despite the low survival rate of this cancer type, the early prediction of this disease has a crucial role in decreasing the mortality rate and improving the prognosis. So, this study. MATERIALS AND METHODS In this retrospective study, we used 654 alive and dead PC cases to establish the prediction model for PC. The six chosen machine learning algorithms and prognostic factors were utilized to build the prediction models. The importance of the predictive factors was assessed using the relative importance of a high-performing algorithm. RESULTS The XG-Boost with AU-ROC of 0.933 (95% CI= [0.906-0.958]) and AU-ROC of 0.836 (95% CI= [0.789-0.865] in internal and external validation modes were considered as the best-performing model for predicting the mortality risk of PC. The factors, including tumor size, smoking, and chemotherapy, were considered the most influential for prediction. CONCLUSION The XG-Boost gained more performance efficiency in predicting the mortality risk of PC patients, so this model can promote the clinical solutions that doctors can achieve in healthcare environments to decrease the mortality risk of these patients.
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Affiliation(s)
- Raoof Nopour
- Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran.
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Xu J, Liu Z, Lu Y, Zheng Z, Zhang X. A machine learning model to predict spontaneous vaginal delivery failure for term nulliparous women: An observational study. Int J Gynaecol Obstet 2024. [PMID: 38899565 DOI: 10.1002/ijgo.15739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 04/17/2024] [Accepted: 04/22/2024] [Indexed: 06/21/2024]
Abstract
OBJECTIVE This study aims to construct and evaluate a model to predict spontaneous vaginal delivery (SVD) failure in term nulliparous women based on machine learning algorithms. METHODS In this retrospective observational study, data on nulliparous women without contraindications for vaginal delivery with a singleton pregnancy ≥37 weeks and before the onset of labor from September 2020 to September 2021 were divided into a training set and a temporal validation set. Transperineal ultrasound was performed to collect angle of progression, head-perineum distance, subpubic arch angle, and their levator hiatal dimensions. The cervical length was measured via transvaginal ultrasound. The delivery methods were later recorded. Through LASSO regression analysis, indicators that can affect SVD failure were selected. Seven common machine learning algorithms were selected for model training, and the optimal algorithm was selected based on the area under the curve (AUC) to evaluate the effectiveness of the validation model. RESULTS Four indicators related to SVD failure were identified through LASSO regression screening: angle of progression, cervical length, subpubic arch angle, and estimated fetal weight. The Gaussian NB algorithm was found to yield the highest AUC (0.82, 95% confidence interval [CI] 0.65-0.98) during model training, and hence it was chosen for verification with the temporal validation set, in which an AUC of 0.79 (95% CI 0.64-0.95) was obtained with accuracy, sensitivity, and specificity rates of 80.9%, 72.7%, and 75.0%, respectively. CONCLUSION The Gaussian NB model showed good predictive effect, proving its potential as a clinical reference for predicting SVD failure of term nulliparous women before actual delivery.
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Affiliation(s)
- Jing Xu
- Department of Ultrasound, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China
| | - Zifeng Liu
- Department of Big Data and Artificial Intelligence, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China
| | - Yaxin Lu
- Department of Big Data and Artificial Intelligence, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China
| | - Zhijuan Zheng
- Department of Ultrasound, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China
| | - Xinling Zhang
- Department of Ultrasound, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China
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Rui F, Xu L, Yeo YH, Xu Y, Ni W, Tan Y, Zheng Q, Tian X, Zeng QL, He Z, Qiu Y, Zhu C, Ding W, Wang J, Huang R, Xue Q, Wang X, Chen Y, Fan J, Fan Z, Ogawa E, Kwak MS, Qi X, Shi J, Wong VWS, Wu C, Li J. Machine Learning-Based Models for Advanced Fibrosis and Cirrhosis Diagnosis in Chronic Hepatitis B Patients With Hepatic Steatosis. Clin Gastroenterol Hepatol 2024:S1542-3565(24)00553-6. [PMID: 38906440 DOI: 10.1016/j.cgh.2024.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 06/10/2024] [Accepted: 06/11/2024] [Indexed: 06/23/2024]
Abstract
BACKGROUND AND AIMS The global rise of chronic hepatitis B (CHB) superimposed on hepatic steatosis (HS) warrants noninvasive, precise tools for assessing fibrosis progression. This study leveraged machine learning (ML) to develop diagnostic models for advanced fibrosis and cirrhosis in this patient population. METHODS Treatment-naive CHB patients with concurrent HS who underwent liver biopsy in 10 medical centers were enrolled as a training cohort and an independent external validation cohort (NCT05766449). Six ML models were implemented to predict advanced fibrosis and cirrhosis. The final models, derived from SHAP (Shapley Additive exPlanations), were compared with Fibrosis-4 Index, nonalcoholic fatty liver disease Fibrosis Score, and aspartate aminotransferase-to-platelet ratio index using the area under receiver-operating characteristic curve (AUROC) and decision curve analysis (DCA). RESULTS Of 1,198 eligible patients, the random forest model achieved AUROCs of 0.778 (95% confidence interval [CI], 0.749-0.807) for diagnosing advanced fibrosis (random forest advanced fibrosis model) and 0.777 (95% CI, 0.748-0.806) for diagnosing cirrhosis (random forest cirrhosis model) in the training cohort, and maintained high AUROCs in the validation cohort. In the training cohort, the random forest advanced fibrosis model obtained an AUROC of 0.825 (95% CI, 0.787-0.862) in patients with hepatitis B virus DNA ≥105 IU/mL, and the random forest cirrhosis model had an AUROC of 0.828 (95% CI, 0.774-0.883) in female patients. The 2 models outperformed Fibrosis-4 Index, nonalcoholic fatty liver disease Fibrosis Score, and aspartate aminotransferase-to-platelet ratio index in the training cohort, and also performed well in the validation cohort. CONCLUSIONS The random forest models provide reliable, noninvasive tools for identifying advanced fibrosis and cirrhosis in CHB patients with concurrent HS, offering a significant advancement in the comanagement of the 2 diseases. CLINICALTRIALS gov, Number: NCT05766449.
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Affiliation(s)
- Fajuan Rui
- Department of Infectious Diseases, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China; Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China; Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, China
| | - Liang Xu
- Clinical School of the Second People's Hospital, Tianjin Medical University, Tianjin, China; Department of Hepatology, Tianjin Second People's Hospital, Tianjin, China; Tianjin Research Institute of Liver Diseases, Tianjin, China
| | - Yee Hui Yeo
- Karsh Division of Gastroenterology and Hepatology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Yayun Xu
- Department of Gastroenterology, West China Tianfu Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wenjing Ni
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China; Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, China
| | - Youwen Tan
- Department of Hepatology, The Third Hospital of Zhenjiang Affiliated Jiangsu University, Zhenjiang, China
| | - Qi Zheng
- Department of Hepatology, Hepatology Research institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China
| | - Xiaorong Tian
- School of Computer Science, China University of Geosciences, Wuhan, China; Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China
| | - Qing-Lei Zeng
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zebao He
- Department of Infectious Diseases, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou, China
| | - Yuanwang Qiu
- Department of Infectious Diseases, The Fifth People's Hospital of Wuxi, Wuxi, China
| | - Chuanwu Zhu
- Department of Infectious Diseases, The Affiliated Infectious Diseases Hospital of Soochow University, Suzhou, China
| | - Weimao Ding
- Department of Hepatology, Huai'an No.4 People's Hospital, Huai'an, China
| | - Jian Wang
- Department of Infectious Diseases, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Rui Huang
- Department of Infectious Diseases, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Qi Xue
- Department of Infectious Diseases, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, China
| | - Xueqi Wang
- Department of Gastroenterology, The First Affiliated Hospital of Shandong Second Medical University, Weifang People's Hospital, Weifang, China
| | - Yunliang Chen
- School of Computer Science, China University of Geosciences, Wuhan, China; Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China
| | - Junqing Fan
- School of Computer Science, China University of Geosciences, Wuhan, China; Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China
| | - Zhiwen Fan
- Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Eiichi Ogawa
- Department of General Internal Medicine, Kyushu University Hospital, Fukuoka, Japan
| | - Min-Sun Kwak
- Department of Internal Medicine, Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea
| | - Xiaolong Qi
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China.
| | - Junping Shi
- Department of Infectious and Hepatology Diseases, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.
| | - Vincent Wai-Sun Wong
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China; State Key Laboratory of Digestive Disease, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Chao Wu
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China; Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, China
| | - Jie Li
- Department of Infectious Diseases, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China; Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China; Institute of Viruses and Infectious Diseases, Nanjing University, Nanjing, China.
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Liu Y, Du L, Li L, Xiong L, Luo H, Kwaku E, Mei X, Wen C, Cui YY, Zhou Y, Zeng L, Li S, Wang K, Zheng J, Liu Z, Hu H, Yue R. Development and validation of a machine learning-based readmission risk prediction model for non-ST elevation myocardial infarction patients after percutaneous coronary intervention. Sci Rep 2024; 14:13393. [PMID: 38862634 PMCID: PMC11166920 DOI: 10.1038/s41598-024-64048-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 06/04/2024] [Indexed: 06/13/2024] Open
Abstract
To investigate the factors that influence readmissions in patients with acute non-ST elevation myocardial infarction (NSTEMI) after percutaneous coronary intervention (PCI) by using multiple machine learning (ML) methods to establish a predictive model. In this study, 1576 NSTEMI patients who were hospitalized at the Affiliated Hospital of North Sichuan Medical College were selected as the research subjects. They were divided into two groups: the readmitted group and the non-readmitted group. The division was based on whether the patients experienced complications or another incident of myocardial infarction within one year after undergoing PCI. Common variables selected by univariate and multivariate logistic regression, LASSO regression, and random forest were used as independent influencing factors for NSTEMI patients' readmissions after PCI. Six different ML models were constructed using these common variables. The area under the ROC curve, accuracy, sensitivity, and specificity were used to evaluate the performance of the six ML models. Finally, the optimal model was selected, and a nomogram was created to visually represent its clinical effectiveness. Three different methods were used to select seven representative common variables. These variables were then utilized to construct six different ML models, which were subsequently compared. The findings indicated that the LR model exhibited the most optimal performance in terms of AUC, accuracy, sensitivity, and specificity. The outcome, admission mode (walking and non-walking), communication ability, CRP, TC, HDL, and LDL were identified as independent predicators of readmissions in NSTEMI patients after PCI. The prediction model constructed by the LR algorithm was the best. The established column graph model established proved to be effective in identifying high-risk groups with high accuracy and differentiation. It holds a specific predictive value for the occurrence of readmissions after direct PCI in NSTEMI patients.
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Affiliation(s)
- Yanxu Liu
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, No. 63, Wenhua Road, Nanchong, 637000, Sichuan Province, People's Republic of China
| | - Linqin Du
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, No. 63, Wenhua Road, Nanchong, 637000, Sichuan Province, People's Republic of China
| | - Lan Li
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, No. 63, Wenhua Road, Nanchong, 637000, Sichuan Province, People's Republic of China
| | - Lijuan Xiong
- Department of Cardiology, People's Hospital of Guang'an District, Guang'an, 638550, People's Republic of China
| | - Hao Luo
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, No. 63, Wenhua Road, Nanchong, 637000, Sichuan Province, People's Republic of China
| | - Eugene Kwaku
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, No. 63, Wenhua Road, Nanchong, 637000, Sichuan Province, People's Republic of China
- Family Health University College and Hospital, Opposite Kofi Annan International Peace Keeping Training Center, Teshie, Accra, Ghana
| | - Xue Mei
- School of Pharmacy, Institute of Material Medica, North Sichuan Medical College, Nanchong, 637000, Sichuan, People's Republic of China
| | - Cong Wen
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, No. 63, Wenhua Road, Nanchong, 637000, Sichuan Province, People's Republic of China
| | - Yang Yang Cui
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, No. 63, Wenhua Road, Nanchong, 637000, Sichuan Province, People's Republic of China
| | - Yang Zhou
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, No. 63, Wenhua Road, Nanchong, 637000, Sichuan Province, People's Republic of China
| | - Lang Zeng
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, No. 63, Wenhua Road, Nanchong, 637000, Sichuan Province, People's Republic of China
| | - Shikang Li
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, No. 63, Wenhua Road, Nanchong, 637000, Sichuan Province, People's Republic of China
| | - Kun Wang
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, No. 63, Wenhua Road, Nanchong, 637000, Sichuan Province, People's Republic of China
| | - Jiankang Zheng
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, No. 63, Wenhua Road, Nanchong, 637000, Sichuan Province, People's Republic of China
| | - Zonglian Liu
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, No. 63, Wenhua Road, Nanchong, 637000, Sichuan Province, People's Republic of China
| | - Houxiang Hu
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, No. 63, Wenhua Road, Nanchong, 637000, Sichuan Province, People's Republic of China
| | - Rongchuan Yue
- Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, No. 63, Wenhua Road, Nanchong, 637000, Sichuan Province, People's Republic of China.
- Department of Cardiology, People's Hospital of Guang'an District, Guang'an, 638550, People's Republic of China.
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Rakhshani Rad S, Mohammadi ZH, Zadeh MJ, Mosleh-Shirazi MA, Dehesh T. Identification of important symptoms and diagnostic hypothyroidism patients using machine learning algorithms. Ann Med Surg (Lond) 2024; 86:3233-3241. [PMID: 38846869 PMCID: PMC11152820 DOI: 10.1097/ms9.0000000000002068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 03/30/2024] [Indexed: 06/09/2024] Open
Abstract
Background Hypothyroidism is one of the most common endocrine diseases. It is, however, usually challenging for physicians to diagnose due to nonspecific symptoms. The usual procedure for diagnosis of Hypothyroidism is a blood test. In recent years, machine learning algorithms have proved to be powerful tools in medicine due to their diagnostic accuracy. In this study, the authors aim to predict and identify the most important symptoms of Hypothyroidism using machine learning algorithms. Method In this cross-sectional, single-center study, 1296 individuals who visited an endocrinologist for the first time with symptoms of Hypothyroidism were studied, 676 of whom were identified as patients through thyroid-stimulating hormone testing. The outcome was binary (with Hypothyroidism /without Hypothyroidism). In a comparative analysis, random forest, decision tree, and logistic regression methods were used to diagnose primary Hypothyroidism. Results Symptoms such as tiredness, unusual cold feeling, yellow skin (jaundice), cold hands and feet, numbness of hands, loss of appetite, and weight Hypothyroidism gain were recognized as the most important symptoms in identifying Hypothyroidism. Among the studied algorithms, random forest had the best performance in identifying these symptoms (accuracy=0.83, kappa=0.46, sensitivity=0.88, specificity=0.88). Conclusions The findings suggest that machine learning methods can identify Hypothyroidism patients who show relatively simple symptoms with acceptable accuracy without the need for a blood test. Greater familiarity and utilization of such methods by physicians may, therefore, reduce the expense and stress burden of clinical testing.
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Affiliation(s)
| | - Zahra H. Mohammadi
- Endocrinology and Metabolism Research Center, Institute of Basic and Clinical Physiology Sciences
| | | | - Mohammad A. Mosleh-Shirazi
- Department of Radio-Oncology
- Ionizing and Non-Ionizing Radiation Protection Research Center (INIRPRC), School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Tania Dehesh
- Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman
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McDaniel CC, Lo-Ciganic WH, Huang J, Chou C. A machine learning model to predict therapeutic inertia in type 2 diabetes using electronic health record data. J Endocrinol Invest 2024; 47:1419-1433. [PMID: 38160431 DOI: 10.1007/s40618-023-02259-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 11/24/2023] [Indexed: 01/03/2024]
Abstract
OBJECTIVE To estimate the therapeutic inertia prevalence for patients with type 2 diabetes, develop and validate a machine learning model predicting therapeutic inertia, and determine the added predictive value of area-level social determinants of health (SDOH). METHODS This prognostic study with a retrospective cohort design used OneFlorida data (linked electronic health records (EHRs) from 1240 practices/clinics in Florida). The study cohort included adults (aged ≥ 18) with type 2 diabetes, HbA1C ≥ 7% (53 mmol/mol), ≥one ambulatory visit, and ≥one antihyperglycemic medication prescribed (excluded patients prescribed insulin before HbA1C). The outcome was therapeutic inertia, defined as absence of treatment intensification within six months after HbA1C ≥ 7% (53 mmol/mol). The predictors were patient, provider, and healthcare system factors. Machine learning methods included gradient boosting machines (GBM), random forests (RF), elastic net (EN), and least absolute shrinkage and selection operator (LASSO). The DeLong test compared the discriminative ability (represented by C-statistics) between models. RESULTS The cohort included 31,087 patients with type 2 diabetes (mean age = 58.89 (SD = 13.27) years, 50.50% male, 58.89% White). The therapeutic inertia prevalence was 39.80% among the 68,445 records. GBM outperformed (C-statistic from testing sample = 0.84, 95% CI = 0.83-0.84) RF (C-statistic = 0.80, 95% CI = 0.79-0.80), EN (C-statistic = 0.80, 95% CI = 0.80-0.81), and LASSO (C-statistic = 0.80, 95% CI = 0.80-0.81), p < 0.05. Area-level SDOH significantly increased the discriminative ability versus models without SDOH (C-statistic for GBM = 0.84, 95% CI = 0.84-0.85 vs. 0.84, 95% CI = 0.83-0.84), p < 0.05. CONCLUSIONS Using EHRs of patients with type 2 diabetes from a large state, machine learning predicted therapeutic inertia (prevalence = 40%). The model's ability to predict patients at high risk of therapeutic inertia is clinically applicable to diabetes care.
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Affiliation(s)
- C C McDaniel
- Department of Health Outcomes Research and Policy, Harrison College of Pharmacy, Auburn University, 4306 Walker Building, Auburn, AL, 36849, USA.
| | - W-H Lo-Ciganic
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA
- Division of General Internal Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Center for Pharmaceutical Policy and Prescribing, University of Pittsburgh, Pittsburgh, PA, USA
- North Florida/South Georgia Veterans Health System, Geriatric Research Education and Clinical Center, Gainesville, FL, USA
| | - J Huang
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA
| | - C Chou
- Department of Health Outcomes Research and Policy, Harrison College of Pharmacy, Auburn University, 4306 Walker Building, Auburn, AL, 36849, USA
- Department of Medical Research, China Medical University Hospital, Taichung City, Taiwan
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Haverkamp W, Strodthoff N. [Artificial intelligence-enhanced electrocardiography : Will it revolutionize diagnosis and management of our patients?]. Herzschrittmacherther Elektrophysiol 2024; 35:104-110. [PMID: 38361131 DOI: 10.1007/s00399-024-00997-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 01/23/2024] [Indexed: 02/17/2024]
Abstract
The use of artificial intelligence (AI) in healthcare has made significant progress in the last 10 years. Many experts believe that utilization of AI technologies, especially deep learning, will bring about drastic changes in how physicians understand, diagnose, and treat diseases. One aspect of this development is AI-enhanced electrocardiography (ECG) analysis. It involves not only optimizing the traditional ECG analysis by the physician and improving the accuracy of automatic interpretation by the ECG device but also introducing entirely new diagnostic options enabled by AI. Examples include assessing left ventricular function, predicting atrial fibrillation, and diagnosing both cardiac and noncardiac conditions. Through AI, the ECG becomes a comprehensive tool for screening, diagnosis, and patient management, potentially revolutionizing clinical practices. This paper provides an overview of the current state of this development, discusses existing limitations, and explores the challenges that may arise for healthcare professionals in this context.
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Affiliation(s)
- Wilhelm Haverkamp
- Abteilung für Kardiologie und Metabolismus, Medizinische Klinik mit Schwerpunkt Kardiologie, Campus Virchow-Klinikum, Deutsches Herzzentrum der Charité, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Deutschland.
| | - Nils Strodthoff
- Department für Versorgungsforschung, Fakultät VI - Medizin und Gesundheitswissenschaften, Abteilung AI4Health, Carl von Ossietzky Universität Oldenburg, Oldenburg, Deutschland
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Mao J, Zhu Z, Xia M, Zhou M, Wang L, Xia J, Wang Z. Enhanced Runge-Kutta-driven feature selection model for early detection of gastroesophageal reflux disease. Comput Biol Med 2024; 175:108394. [PMID: 38657464 DOI: 10.1016/j.compbiomed.2024.108394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/12/2024] [Accepted: 04/01/2024] [Indexed: 04/26/2024]
Abstract
Gastroesophageal reflux disease (GERD) profoundly compromises the quality of life, with prolonged untreated cases posing a heightened risk of severe complications such as esophageal injury and esophageal carcinoma. The imperative for early diagnosis is paramount in averting progressive pathological developments. This study introduces a wrapper-based feature selection model based on the enhanced Runge Kutta algorithm (SCCRUN) and fuzzy k-nearest neighbors (FKNN) for GERD prediction, named bSCCRUN-FKNN-FS. Runge Kutta algorithm (RUN) is a metaheuristic algorithm designed based on the Runge-Kutta method. However, RUN's effectiveness in local search capabilities is insufficient, and it exhibits insufficient convergence accuracy. To enhance the convergence accuracy of RUN, spiraling communication and collaboration (SCC) is introduced. By facilitating information exchange among population individuals, SCC expands the solution search space, thereby improving convergence accuracy. The optimization capabilities of SCCRUN are experimentally validated through comparisons with classical and state-of-the-art algorithms on the IEEE CEC 2017 benchmark. Subsequently, based on SCCRUN, the bSCCRUN-FKNN-FS model is proposed. During the period from 2019 to 2023, a dataset comprising 179 cases of GERD, including 110 GERD patients and 69 healthy individuals, was collected from Zhejiang Provincial People's Hospital. This dataset was utilized to compare our proposed model against similar algorithms in order to evaluate its performance. Concurrently, it was determined that features such as the internal diameter of the esophageal hiatus during distention, esophagogastric junction diameter during distention, and external diameter of the esophageal hiatus during non-distention play crucial roles in influencing GERD prediction. Experimental findings demonstrate the outstanding performance of the proposed model, with a predictive accuracy reaching as high as 93.824 %. These results underscore the significant advantage of the proposed model in both identifying and predicting GERD patients.
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Affiliation(s)
- Jinlei Mao
- General Surgery, Cancer Center, Department of Hernia Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital). Hangzhou Medical College, Hangzhou, Zhejiang, China.
| | - Zhihao Zhu
- General Surgery, Cancer Center, Department of Hernia Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital). Hangzhou Medical College, Hangzhou, Zhejiang, China.
| | - Minjun Xia
- General Surgery, Cancer Center, Department of Hernia Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital). Hangzhou Medical College, Hangzhou, Zhejiang, China.
| | - Menghui Zhou
- General Surgery, Cancer Center, Department of Hernia Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital). Hangzhou Medical College, Hangzhou, Zhejiang, China.
| | - Li Wang
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
| | - Jianfu Xia
- Department of General Surgery, The Dingli Clinical College of Wenzhou Medical University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Zhifei Wang
- General Surgery, Cancer Center, Department of Hernia Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital). Hangzhou Medical College, Hangzhou, Zhejiang, China.
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Biesheuvel LA, Dongelmans DA, Elbers PW. Artificial intelligence to advance acute and intensive care medicine. Curr Opin Crit Care 2024; 30:246-250. [PMID: 38525882 PMCID: PMC11064910 DOI: 10.1097/mcc.0000000000001150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
PURPOSE OF REVIEW This review explores recent key advancements in artificial intelligence for acute and intensive care medicine. As artificial intelligence rapidly evolves, this review aims to elucidate its current applications, future possibilities, and the vital challenges that are associated with its integration into emergency medical dispatch, triage, medical consultation and ICUs. RECENT FINDINGS The integration of artificial intelligence in emergency medical dispatch (EMD) facilitates swift and accurate assessment. In the emergency department (ED), artificial intelligence driven triage models leverage diverse patient data for improved outcome predictions, surpassing human performance in retrospective studies. Artificial intelligence can streamline medical documentation in the ED and enhances medical imaging interpretation. The introduction of large multimodal generative models showcases the future potential to process varied biomedical data for comprehensive decision support. In the ICU, artificial intelligence applications range from early warning systems to treatment suggestions. SUMMARY Despite promising academic strides, widespread artificial intelligence adoption in acute and critical care is hindered by ethical, legal, technical, organizational, and validation challenges. Despite these obstacles, artificial intelligence's potential to streamline clinical workflows is evident. When these barriers are overcome, future advancements in artificial intelligence have the potential to transform the landscape of patient care for acute and intensive care medicine.
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Affiliation(s)
- Laurens A. Biesheuvel
- Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence (C4I), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam Public Health (APH), Amsterdam UMC
- Quantitative Data Analytics Group, Department of Computer Science, Faculty of Science, Vrije Universiteit
| | - Dave A. Dongelmans
- Department of Intensive Care Medicine, Amsterdam Public Health (APH), Amsterdam UMC, University of Amsterdam
- National Intensive Care Evaluation Foundation, Amsterdam, The Netherlands
| | - Paul W.G. Elbers
- Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence (C4I), Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Science (ACS), Amsterdam Institute for Infection and Immunity (AII), Amsterdam Public Health (APH), Amsterdam UMC
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Miyagi Y, Iwashima S. Prediction Models for Intravenous Immunoglobulin Non-Responders of Kawasaki Disease Using Machine Learning. Clin Drug Investig 2024; 44:425-437. [PMID: 38869717 DOI: 10.1007/s40261-024-01373-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/27/2024] [Indexed: 06/14/2024]
Abstract
BACKGROUND AND OBJECTIVE: Intravenous immunoglobulin (IVIG) is a prominent therapeutic agent for Kawasaki disease (KD) that significantly reduces the incidence of coronary artery anomalies. Various methodologies, including machine learning, have been employed to develop IVIG non-responder prediction models; however, their validation and reproducibility remain unverified. This study aimed to develop a predictive scoring system for identifying IVIG nonresponders and rigorously test the accuracy and reliability of this system. METHODS: The study included an exposure group of 228 IVIG non-responders and a control group of 997 IVIG responders. Subsequently, a predictive machine learning model was constructed. The Shizuoka score, including variables such as the "initial treatment date" (cutoff: < 4 days), sodium level (cutoff: < 133 mEq/L), total bilirubin level (cutoff: ≥ 0.5 mg/dL), and neutrophil-to-lymphocyte ratio (cutoff: ≥ 2.6), was established. Patients meeting two or more of these criteria were grouped as high-risk IVIG non-responders. Using the Shizuoka score to stratify IVIG responders, propensity score matching was used to analyze 85 patients each for IVIG and IVIG-added prednisolone treatment in the high-risk group. In the IVIG plus prednisolone group, the IVIG non-responder count significantly decreased (p < 0.001), with an odds ratio of 0.192 (95% confidence interval 0.078-0.441). CONCLUSIONS: Intravenous immunoglobulin non-responders were predicted using machine learning models and validated using propensity score matching. The initiation of initial IVIG-added prednisolone treatment in the high-risk group identified by the Shizuoka score, crafted using machine learning models, appears useful for predicting IVIG non-responders.
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Affiliation(s)
- Yoshifumi Miyagi
- Department of Pediatrics, Haibara Hospital, Makinohara City, Shizuoka, Japan
| | - Satoru Iwashima
- The Shizuoka Kawasaki Disease Study Group, Shizuoka, Japan.
- Department of Pediatrics, Chutoen General Medical Center, 1-1 Shobugaike, Kakegawa, Shizuoka, 436-0040, Japan.
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Wu J, Li T, Xu L, Chen L, Liang X, Lin A, Zhang W, Huang R. Development of a machine learning-based prediction model for clinical pregnancy of intrauterine insemination in a large Chinese population. J Assist Reprod Genet 2024:10.1007/s10815-024-03153-2. [PMID: 38819714 DOI: 10.1007/s10815-024-03153-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 05/22/2024] [Indexed: 06/01/2024] Open
Abstract
PURPOSE This study aimed to evaluate the effectiveness of a random forest (RF) model in predicting clinical pregnancy outcomes from intrauterine insemination (IUI) and identifying significant factors affecting IUI pregnancy in a large Chinese population. METHODS RESULTS: A total of 11 variables, including eight from female (age, body mass index, duration of infertility, prior miscarriage, and spontaneous abortion), hormone levels (anti-Müllerian hormone, follicle-stimulating hormone, luteinizing hormone), and three from male (smoking, semen volume, and sperm concentration), were identified as the significant variables associated with IUI clinical pregnancy in our Chinese dataset. The RF-based prediction model presents an area under the receiver operating characteristic curve (AUC) of 0.716 (95% confidence interval, 0.6914-0.7406), an accuracy rate of 0.6081, a sensitivity rate of 0.7113, and a specificity rate of 0.505. Importance analysis indicated that semen volume was the most vital variable in predicting IUI clinical pregnancy. CONCLUSIONS The machine learning-based IUI clinical pregnancy prediction model showed a promising predictive efficacy that could provide a potent tool to guide selecting targeted infertile couples beneficial from IUI treatment, and also identify which parameters are most relevant in IUI clinical pregnancy.
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Affiliation(s)
- Jialin Wu
- Reproductive Medicine Center, Sixth Affiliated Hospital, Sun Yat-Sen University, Shou Gou Ling Road, Guangzhou, 510000, China
- Guangdong Engineering Technology Research Center of Fertility Preservation, Guangzhou, 510000, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510000, China
- School of Public Health, Sun Yat-Sen University, No. 74 Zhongshan Second Road, Guangzhou, 510000, China
| | - Tingting Li
- Reproductive Medicine Center, Sixth Affiliated Hospital, Sun Yat-Sen University, Shou Gou Ling Road, Guangzhou, 510000, China
- Guangdong Engineering Technology Research Center of Fertility Preservation, Guangzhou, 510000, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510000, China
| | - Linan Xu
- Reproductive Medicine Center, Sixth Affiliated Hospital, Sun Yat-Sen University, Shou Gou Ling Road, Guangzhou, 510000, China
- Guangdong Engineering Technology Research Center of Fertility Preservation, Guangzhou, 510000, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510000, China
| | - Lina Chen
- Reproductive Medicine Center, Sixth Affiliated Hospital, Sun Yat-Sen University, Shou Gou Ling Road, Guangzhou, 510000, China
- Guangdong Engineering Technology Research Center of Fertility Preservation, Guangzhou, 510000, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510000, China
| | - Xiaoyan Liang
- Reproductive Medicine Center, Sixth Affiliated Hospital, Sun Yat-Sen University, Shou Gou Ling Road, Guangzhou, 510000, China
- Guangdong Engineering Technology Research Center of Fertility Preservation, Guangzhou, 510000, China
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510000, China
| | - Aihua Lin
- School of Public Health, Sun Yat-Sen University, No. 74 Zhongshan Second Road, Guangzhou, 510000, China
| | - Wangjian Zhang
- School of Public Health, Sun Yat-Sen University, No. 74 Zhongshan Second Road, Guangzhou, 510000, China.
| | - Rui Huang
- Reproductive Medicine Center, Sixth Affiliated Hospital, Sun Yat-Sen University, Shou Gou Ling Road, Guangzhou, 510000, China.
- Guangdong Engineering Technology Research Center of Fertility Preservation, Guangzhou, 510000, China.
- Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510000, China.
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Cheng C, Liang X, Guo D, Xie D. Application of Artificial Intelligence in Shoulder Pathology. Diagnostics (Basel) 2024; 14:1091. [PMID: 38893618 PMCID: PMC11171621 DOI: 10.3390/diagnostics14111091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 05/16/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024] Open
Abstract
Artificial intelligence (AI) refers to the science and engineering of creating intelligent machines for imitating and expanding human intelligence. Given the ongoing evolution of the multidisciplinary integration trend in modern medicine, numerous studies have investigated the power of AI to address orthopedic-specific problems. One particular area of investigation focuses on shoulder pathology, which is a range of disorders or abnormalities of the shoulder joint, causing pain, inflammation, stiffness, weakness, and reduced range of motion. There has not yet been a comprehensive review of the recent advancements in this field. Therefore, the purpose of this review is to evaluate current AI applications in shoulder pathology. This review mainly summarizes several crucial stages of the clinical practice, including predictive models and prognosis, diagnosis, treatment, and physical therapy. In addition, the challenges and future development of AI technology are also discussed.
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Affiliation(s)
- Cong Cheng
- Department of Orthopaedics, People’s Hospital of Longhua, Shenzhen 518000, China;
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Xinzhi Liang
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Dong Guo
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
| | - Denghui Xie
- Department of Joint Surgery and Sports Medicine, Center for Orthopedic Surgery, Orthopedic Hospital of Guangdong Province, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China; (X.L.); (D.G.)
- Guangdong Provincial Key Laboratory of Bone and Joint Degeneration Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou 510630, China
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Wang L, Xu Y, Yang R, Yang G, Chi H. Letter to the editor for the article "Combination of urinary biomarkers and machine-learning models provided a higher predictive accuracy to predict long-term treatment outcomes of patients with interstitial cystitis/bladder pain syndrome". World J Urol 2024; 42:351. [PMID: 38787421 DOI: 10.1007/s00345-024-05044-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 05/06/2024] [Indexed: 05/25/2024] Open
Affiliation(s)
- Lexin Wang
- General Hospital of Ningxia Medical University, Yinchuan, Ningxia, 750000, China
- Ningxia Medical University, Yinchuan, Ningxia, 750000, China
| | - Yaoqin Xu
- General Hospital of Ningxia Medical University, Yinchuan, Ningxia, 750000, China
- Ningxia Medical University, Yinchuan, Ningxia, 750000, China
| | - Runqin Yang
- General Hospital of Ningxia Medical University, Yinchuan, Ningxia, 750000, China
- Ningxia Medical University, Yinchuan, Ningxia, 750000, China
| | - Guanhu Yang
- Department of Specialty Medicine, Ohio University, Athens, OH, 45701, USA.
| | - Hao Chi
- Clinical Medicine College, Southwest Medical University, Luzhou, China.
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Javidi H, Mariam A, Alkhaled L, Pantalone KM, Rotroff DM. An interpretable predictive deep learning platform for pediatric metabolic diseases. J Am Med Inform Assoc 2024; 31:1227-1238. [PMID: 38497983 PMCID: PMC11105121 DOI: 10.1093/jamia/ocae049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 02/20/2024] [Accepted: 02/27/2024] [Indexed: 03/19/2024] Open
Abstract
OBJECTIVES Metabolic disease in children is increasing worldwide and predisposes a wide array of chronic comorbid conditions with severe impacts on quality of life. Tools for early detection are needed to promptly intervene to prevent or slow the development of these long-term complications. MATERIALS AND METHODS No clinically available tools are currently in widespread use that can predict the onset of metabolic diseases in pediatric patients. Here, we use interpretable deep learning, leveraging longitudinal clinical measurements, demographical data, and diagnosis codes from electronic health record data from a large integrated health system to predict the onset of prediabetes, type 2 diabetes (T2D), and metabolic syndrome in pediatric cohorts. RESULTS The cohort included 49 517 children with overweight or obesity aged 2-18 (54.9% male, 73% Caucasian), with a median follow-up time of 7.5 years and mean body mass index (BMI) percentile of 88.6%. Our model demonstrated area under receiver operating characteristic curve (AUC) accuracies up to 0.87, 0.79, and 0.79 for predicting T2D, metabolic syndrome, and prediabetes, respectively. Whereas most risk calculators use only recently available data, incorporating longitudinal data improved AUCs by 13.04%, 11.48%, and 11.67% for T2D, syndrome, and prediabetes, respectively, versus models using the most recent BMI (P < 2.2 × 10-16). DISCUSSION Despite most risk calculators using only the most recent data, incorporating longitudinal data improved the model accuracies because utilizing trajectories provides a more comprehensive characterization of the patient's health history. Our interpretable model indicated that BMI trajectories were consistently identified as one of the most influential features for prediction, highlighting the advantages of incorporating longitudinal data when available.
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Affiliation(s)
- Hamed Javidi
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, United States
- Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH 44115, United States
- Center for Quantitative Metabolic Research, Cleveland Clinic, Cleveland, OH 44195, United States
| | - Arshiya Mariam
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, United States
- Center for Quantitative Metabolic Research, Cleveland Clinic, Cleveland, OH 44195, United States
| | - Lina Alkhaled
- Center for Quantitative Metabolic Research, Cleveland Clinic, Cleveland, OH 44195, United States
- Endocrinology and Metabolism Institute, Cleveland Clinic, Cleveland, OH 44195, United States
| | - Kevin M Pantalone
- Center for Quantitative Metabolic Research, Cleveland Clinic, Cleveland, OH 44195, United States
- Endocrinology and Metabolism Institute, Cleveland Clinic, Cleveland, OH 44195, United States
| | - Daniel M Rotroff
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, United States
- Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH 44115, United States
- Center for Quantitative Metabolic Research, Cleveland Clinic, Cleveland, OH 44195, United States
- Endocrinology and Metabolism Institute, Cleveland Clinic, Cleveland, OH 44195, United States
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Goodman D, Zhu AY. Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review. FRONTIERS IN OPHTHALMOLOGY 2024; 4:1380701. [PMID: 38984114 PMCID: PMC11182163 DOI: 10.3389/fopht.2024.1380701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 04/23/2024] [Indexed: 07/11/2024]
Abstract
Introduction The application of artificial intelligence (AI) systems in ophthalmology is rapidly expanding. Early detection and management of keratoconus is important for preventing disease progression and the need for corneal transplant. We review studies regarding the utility of AI in the diagnosis and management of keratoconus and other corneal ectasias. Methods We conducted a systematic search for relevant original, English-language research studies in the PubMed, Web of Science, Embase, and Cochrane databases from inception to October 31, 2023, using a combination of the following keywords: artificial intelligence, deep learning, machine learning, keratoconus, and corneal ectasia. Case reports, literature reviews, conference proceedings, and editorials were excluded. We extracted the following data from each eligible study: type of AI, input used for training, output, ground truth or reference, dataset size, availability of algorithm/model, availability of dataset, and major study findings. Results Ninety-three original research studies were included in this review, with the date of publication ranging from 1994 to 2023. The majority of studies were regarding the use of AI in detecting keratoconus or subclinical keratoconus (n=61). Among studies regarding keratoconus diagnosis, the most common inputs were corneal topography, Scheimpflug-based corneal tomography, and anterior segment-optical coherence tomography. This review also summarized 16 original research studies regarding AI-based assessment of severity and clinical features, 7 studies regarding the prediction of disease progression, and 6 studies regarding the characterization of treatment response. There were only three studies regarding the use of AI in identifying susceptibility genes involved in the etiology and pathogenesis of keratoconus. Discussion Algorithms trained on Scheimpflug-based tomography seem promising tools for the early diagnosis of keratoconus that can be particularly applied in low-resource communities. Future studies could investigate the application of AI models trained on multimodal patient information for staging keratoconus severity and tracking disease progression.
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Chagas OJ, Gonçalves FAR, Nagatomo PP, Buccheri R, Pereira-Chioccola VL, Del Negro GMB, Benard G. Predictive models-assisted diagnosis of AIDS-associated Pneumocystis jirovecii pneumonia in the emergency room, based on clinical, laboratory, and radiological data. Sci Rep 2024; 14:11247. [PMID: 38755293 PMCID: PMC11099134 DOI: 10.1038/s41598-024-61174-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 05/02/2024] [Indexed: 05/18/2024] Open
Abstract
We assessed predictive models (PMs) for diagnosing Pneumocystis jirovecii pneumonia (PCP) in AIDS patients seen in the emergency room (ER), aiming to guide empirical treatment decisions. Data from suspected PCP cases among AIDS patients were gathered prospectively at a reference hospital's ER, with diagnoses later confirmed through sputum PCR analysis. We compared clinical, laboratory, and radiological data between PCP and non-PCP groups, using the Boruta algorithm to confirm significant differences. We evaluated ten PMs tailored for various ERs resource levels to diagnose PCP. Four scenarios were created, two based on X-ray findings (diffuse interstitial infiltrate) and two on CT scans ("ground-glass"), incorporating mandatory variables: lactate dehydrogenase, O2sat, C-reactive protein, respiratory rate (> 24 bpm), and dry cough. We also assessed HIV viral load and CD4 cell count. Among the 86 patients in the study, each model considered either 6 or 8 parameters, depending on the scenario. Many models performed well, with accuracy, precision, recall, and AUC scores > 0.8. Notably, nearest neighbor and naïve Bayes excelled (scores > 0.9) in specific scenarios. Surprisingly, HIV viral load and CD4 cell count did not improve model performance. In conclusion, ER-based PMs using readily available data can significantly aid PCP treatment decisions in AIDS patients.
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Affiliation(s)
- Oscar José Chagas
- Laboratório de Micologia Médica (LIM53), Instituto de Medicina Tropical (IMT), Faculdade de Medicina (FMUSP), Universidade de São Paulo, São Paulo, SP, Brazil.
| | - Fabio Augusto Rodrigues Gonçalves
- Laboratório de Medicina Laboratorial (LIM03), Hospital das Clínicas da Faculdade de Medicina (HCFMUSP), Universidade de São Paulo, São Paulo, SP, Brazil
| | - Priscila Paiva Nagatomo
- Laboratório de Micologia Médica (LIM53), Instituto de Medicina Tropical (IMT), Faculdade de Medicina (FMUSP), Universidade de São Paulo, São Paulo, SP, Brazil
| | - Renata Buccheri
- Instituto de Infectologia Emílio Ribas, São Paulo, SP, Brazil
- Vitalant Research Institute, San Francisco, CA, USA
| | - Vera Lucia Pereira-Chioccola
- Laboratório de Biologia Molecular de Parasitas e Fungos do Centro de Parasitologia e Micologia, Instituto Adolfo Lutz, São Paulo, SP, Brazil
| | - Gilda Maria Barbaro Del Negro
- Laboratório de Micologia Médica (LIM53), Instituto de Medicina Tropical (IMT), Faculdade de Medicina (FMUSP), Universidade de São Paulo, São Paulo, SP, Brazil
| | - Gil Benard
- Laboratório de Micologia Médica (LIM53), Instituto de Medicina Tropical (IMT), Faculdade de Medicina (FMUSP), Universidade de São Paulo, São Paulo, SP, Brazil
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Salkić N, Jovanović P, Barišić Jaman M, Selimović N, Paštrović F, Grgurević I. Machine Learning for Short-Term Mortality in Acute Decompensation of Liver Cirrhosis: Better than MELD Score. Diagnostics (Basel) 2024; 14:981. [PMID: 38786278 PMCID: PMC11119188 DOI: 10.3390/diagnostics14100981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 04/28/2024] [Accepted: 05/06/2024] [Indexed: 05/25/2024] Open
Abstract
Prediction of short-term mortality in patients with acute decompensation of liver cirrhosis could be improved. We aimed to develop and validate two machine learning (ML) models for predicting 28-day and 90-day mortality in patients hospitalized with acute decompensated liver cirrhosis. We trained two artificial neural network (ANN)-based ML models using a training sample of 165 out of 290 (56.9%) patients, and then tested their predictive performance against Model of End-stage Liver Disease-Sodium (MELD-Na) and MELD 3.0 scores using a different validation sample of 125 out of 290 (43.1%) patients. The area under the ROC curve (AUC) for predicting 28-day mortality for the ML model was 0.811 (95%CI: 0.714- 0.907; p < 0.001), while the AUC for the MELD-Na score was 0.577 (95%CI: 0.435-0.720; p = 0.226) and for MELD 3.0 was 0.600 (95%CI: 0.462-0.739; p = 0.117). The area under the ROC curve (AUC) for predicting 90-day mortality for the ML model was 0.839 (95%CI: 0.776- 0.884; p < 0.001), while the AUC for the MELD-Na score was 0.682 (95%CI: 0.575-0.790; p = 0.002) and for MELD 3.0 was 0.703 (95%CI: 0.590-0.816; p < 0.001). Our study demonstrates that ML-based models for predicting short-term mortality in patients with acute decompensation of liver cirrhosis perform significantly better than MELD-Na and MELD 3.0 scores in a validation cohort.
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Affiliation(s)
- Nermin Salkić
- Department of Internal Medicine, School of Medicine, University of Tuzla, 75000 Tuzla, Bosnia and Herzegovina;
| | - Predrag Jovanović
- Department of Internal Medicine, School of Medicine, University of Tuzla, 75000 Tuzla, Bosnia and Herzegovina;
- Department of Gastroenterology and Hepatology, University Clinical Center Tuzla, 75000 Tuzla, Bosnia and Herzegovina;
| | - Mislav Barišić Jaman
- Department for Gastroenterology, Hepatology and Clinical Nutrition, School of Medicine, University of Zagreb, University Hospital Dubrava, 10000 Zagreb, Croatia; (M.B.J.)
| | - Nedim Selimović
- Department of Gastroenterology and Hepatology, University Clinical Center Tuzla, 75000 Tuzla, Bosnia and Herzegovina;
| | - Frane Paštrović
- Department for Gastroenterology, Hepatology and Clinical Nutrition, School of Medicine, University of Zagreb, University Hospital Dubrava, 10000 Zagreb, Croatia; (M.B.J.)
| | - Ivica Grgurević
- Department for Gastroenterology, Hepatology and Clinical Nutrition, School of Medicine, University of Zagreb, University Hospital Dubrava, 10000 Zagreb, Croatia; (M.B.J.)
- Faculty of Pharmacy and Biochemistry, University of Zagreb, 10000 Zagreb, Croatia
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Karimi AH, Langberg J, Malige A, Rahman O, Abboud JA, Stone MA. Accuracy of machine learning to predict the outcomes of shoulder arthroplasty: a systematic review. ARTHROPLASTY 2024; 6:26. [PMID: 38702749 PMCID: PMC11069283 DOI: 10.1186/s42836-024-00244-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 02/26/2024] [Indexed: 05/06/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) uses computer systems to simulate cognitive capacities to accomplish goals like problem-solving and decision-making. Machine learning (ML), a branch of AI, makes algorithms find connections between preset variables, thereby producing prediction models. ML can aid shoulder surgeons in determining which patients may be susceptible to worse outcomes and complications following shoulder arthroplasty (SA) and align patient expectations following SA. However, limited literature is available on ML utilization in total shoulder arthroplasty (TSA) and reverse TSA. METHODS A systematic literature review in accordance with PRISMA guidelines was performed to identify primary research articles evaluating ML's ability to predict SA outcomes. With duplicates removed, the initial query yielded 327 articles, and after applying inclusion and exclusion criteria, 12 articles that had at least 1 month follow-up time were included. RESULTS ML can predict 30-day postoperative complications with a 90% accuracy, postoperative range of motion with a higher-than-85% accuracy, and clinical improvement in patient-reported outcome measures above minimal clinically important differences with a 93%-99% accuracy. ML can predict length of stay, operative time, discharge disposition, and hospitalization costs. CONCLUSION ML can accurately predict outcomes and complications following SA and healthcare utilization. Outcomes are highly dependent on the type of algorithms used, data input, and features selected for the model. LEVEL OF EVIDENCE III.
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Affiliation(s)
- Amir H Karimi
- Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
| | - Joshua Langberg
- Herbert Wertheim College of Medicine, Miami, FL, 33199, USA
- Department of Orthopaedic Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Ajith Malige
- Department of Orthopaedic Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Omar Rahman
- Department of Orthopaedic Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Joseph A Abboud
- Department of Orthopaedic Surgery, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Michael A Stone
- Department of Orthopaedic Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
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Bal T, Dirican E. A potential new way to facilitate HCV elimination: The prediction of viremia in anti-HCV seropositive patients using machine learning algorithms. Arab J Gastroenterol 2024; 25:223-229. [PMID: 38705815 DOI: 10.1016/j.ajg.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Revised: 01/19/2024] [Accepted: 03/20/2024] [Indexed: 05/07/2024]
Abstract
BACKGROUND AND STUDY AIMS The present study was undertaken to design a new machine learning (ML) model that can predict the presence of viremia in hepatitis C virus (HCV) antibody (anti-HCV) seropositive cases. PATIENTS AND METHODS This retrospective study was conducted between January 2012-January 2022 with 812 patients who were referred for anti-HCV positivity and were examined for HCV ribonucleic acid (HCV RNA). Models were constructed with 11 features with a predictor (presence and absence of viremia) to predict HCV viremia. To build an optimal model, this current study also examined and compared the three classifier data mining approaches: RF, SVM and XGBoost. RESULTS The highest performance was achieved with XGBoost (90%), which was followed by RF (89%), SVM Linear (85%) and SVM Radial (83%) algorithms, respectively. The four most important key features contributing to the models were: alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin (ALB) and anti-HCV levels, respectively, while "ALB" was replaced by the "AGE" only in the XGBoost model. CONCLUSION This study has shown that XGBoost and RF based ML models, incorporating anti-HCV levels and routine laboratory tests (ALT, AST, ALB), and age are capable of providing HCV viremia diagnosis with 90% and 89% accuracy, respectively. These findings highlight the potential of ML models in the early diagnosis of HCV viremia, which may be helpful in optimizing HCV elimination programs.
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Affiliation(s)
- Tayibe Bal
- Department of Infectious Diseases and Clinical Microbiology, Faculty of Medicine, Abant Izzet Baysal University, Bolu, Turkey.
| | - Emre Dirican
- Department of Biostatistics, Faculty of Medicine, Hatay Mustafa Kemal University, Hatay, Turkey
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Jiang F, Tao Z, Zhang Y, Xie X, Bao Y, Hu Y, Ding J, Wu C. Machine learning combined with single-cell analysis reveals predictive capacity and immunotherapy response of T cell exhaustion-associated lncRNAs in uterine corpus endometrial carcinoma. Cell Signal 2024; 117:111077. [PMID: 38311301 DOI: 10.1016/j.cellsig.2024.111077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 12/24/2023] [Accepted: 02/01/2024] [Indexed: 02/10/2024]
Abstract
BACKGROUND The exhaustion of T-cells is a primary factor contributing to immune dysfunction in cancer. Long non-coding RNAs (lncRNAs) play a significant role in the advancement, survival, and treatment of Uterine Corpus Endometrial Carcinoma (UCEC). Nevertheless, there has been no investigation into the involvement of lncRNAs associated with T-cell exhaustion (TEXLs) in UCEC. The goal of this work is to establish predictive models for TEXLs in UCEC and study their related immune features. METHODS Using transcriptome and single-cell sequencing data from The Cancer Genome Atlas and Gene Expression Omnibus databases, we employed co-expression analysis and univariate Cox regression to identify prognostic-associated TEXLs (pTEXLs). The prognostic model was developed using the Least Absolute Contraction and Selection Operator. The immunotherapy characteristics of the prognostic model risk score were studied. Then molecular subgroups were identified through non-negative Matrix Factorization based on pTEXLs. The identification of co-expressed genes was done using a weighted correlation network analysis. Subsequently, a diagnostic model for UCEC was created. In-depth investigations, both in vitro and in vivo, were carried out to elucidate the molecular mechanism of the key gene within the diagnostic model. RESULTS Receiver operating characteristic curve, calibration curve, and decision curve analysis proved the validity of the predictive models established according to pTEXLs. The subgroup with lower risk scores in the prognostic model has better responses to blocking immune checkpoint therapy. Single-cell analysis suggests that the expression level of MIEN1 is relatively high in immune cells among diagnostic genes. Furthermore, the targeted suppression of MIEN1 via sh-MIEN1 diminishes the proliferative, migratory, and invasive capacities of UCEC cells, potentially associated with CD8+ T cell exhaustion. CONCLUSIONS The association between TEXLs and UCEC was methodically elucidated by our investigation. A stable pTEXLs risk prediction model and a diagnosis model for UCEC were also established.
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Affiliation(s)
- Feng Jiang
- Department of Neonatology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - Ziyu Tao
- Department of Ultrasound, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - Yun Zhang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaoyan Xie
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yunlei Bao
- Department of Neonatology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China
| | - Yifang Hu
- Department of Geriatric Endocrinology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
| | - Jingxin Ding
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China; Shanghai Key Laboratory of Female Reproductive Endocrine-Related Disease, Shanghai, China.
| | - Chuyan Wu
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
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Binson VA, Thomas S, Subramoniam M, Arun J, Naveen S, Madhu S. A Review of Machine Learning Algorithms for Biomedical Applications. Ann Biomed Eng 2024; 52:1159-1183. [PMID: 38383870 DOI: 10.1007/s10439-024-03459-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 01/24/2024] [Indexed: 02/23/2024]
Abstract
As the amount and complexity of biomedical data continue to increase, machine learning methods are becoming a popular tool in creating prediction models for the underlying biomedical processes. Although all machine learning methods aim to fit models to data, the methodologies used can vary greatly and may seem daunting at first. A comprehensive review of various machine learning algorithms per biomedical applications is presented. The key concepts of machine learning are supervised and unsupervised learning, feature selection, and evaluation metrics. Technical insights on the major machine learning methods such as decision trees, random forests, support vector machines, and k-nearest neighbors are analyzed. Next, the dimensionality reduction methods like principal component analysis and t-distributed stochastic neighbor embedding methods, and their applications in biomedical data analysis were reviewed. Moreover, in biomedical applications predominantly feedforward neural networks, convolutional neural networks, and recurrent neural networks are utilized. In addition, the identification of emerging directions in machine learning methodology will serve as a useful reference for individuals involved in biomedical research, clinical practice, and related professions who are interested in understanding and applying machine learning algorithms in their research or practice.
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Affiliation(s)
- V A Binson
- Department of Electronics Engineering, Saintgits College of Engineering, Kottayam, India
| | - Sania Thomas
- Department of Computer Science and Engineering, Saintgits College of Engineering, Kottayam, India
| | - M Subramoniam
- Department of Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
| | - J Arun
- Centre for Waste Management-International Research Centre, Sathyabama Institute of Science and Technology, Chennai, 600119, India
| | - S Naveen
- Department of Automobile Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India
| | - S Madhu
- Department of Automobile Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
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Rhee TM, Ko YK, Kim HK, Lee SB, Kim BS, Choi HM, Hwang IC, Park JB, Yoon YE, Kim YJ, Cho GY. Machine Learning-Based Discrimination of Cardiovascular Outcomes in Patients With Hypertrophic Cardiomyopathy. JACC. ASIA 2024; 4:375-386. [PMID: 38765660 PMCID: PMC11099823 DOI: 10.1016/j.jacasi.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 11/07/2023] [Accepted: 12/04/2023] [Indexed: 05/22/2024]
Abstract
Background Current risk stratification strategies for patients with hypertrophic cardiomyopathy (HCM) are limited to traditional methodologies. Objectives The authors aimed to establish machine learning (ML)-based models to discriminate major cardiovascular events in patients with HCM. Methods We enrolled consecutive HCM patients from 2 tertiary referral centers and used 25 clinical and echocardiographic features to discriminate major adverse cardiovascular events (MACE), including all-cause death, admission for heart failure (HF-adm), and stroke. The best model was selected for each outcome using the area under the receiver operating characteristic curve (AUROC) with 20-fold cross-validation. After testing in the external validation cohort, the relative importance of features in discriminating each outcome was determined using the SHapley Additive exPlanations (SHAP) method. Results In total, 2,111 patients with HCM (age 61.4 ± 13.6 years; 67.6% men) were analyzed. During the median 4.0 years of follow-up, MACE occurred in 341 patients (16.2%). Among the 4 ML models, the logistic regression model achieved the best AUROC of 0.800 (95% CI: 0.760-0.841) for MACE, 0.789 (95% CI: 0.736-0.841) for all-cause death, 0.798 (95% CI: 0.736-0.860) for HF-adm, and 0.807 (95% CI: 0.754-0.859) for stroke. The discriminant ability of the logistic regression model remained excellent when applied to the external validation cohort for MACE (AUROC = 0.768), all-cause death (AUROC = 0.750), and HF-adm (AUROC = 0.806). The SHAP analysis identified left atrial diameter and hypertension as important variables for all outcomes of interest. Conclusions The proposed ML models incorporating various phenotypes from patients with HCM accurately discriminated adverse cardiovascular events and provided variables with high importance for each outcome.
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Affiliation(s)
- Tae-Min Rhee
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea
| | - Yeon-Kyoung Ko
- Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul, Republic of Korea
- Department of Medical Informatics, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Hyung-Kwan Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Seung-Bo Lee
- Department of Medical Informatics, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Bong-Seong Kim
- Department of Statistics and Actuarial Science, The Soongsil University, Seoul, Republic of Korea
| | - Hong-Mi Choi
- Cardiovascular Center and Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - In-Chang Hwang
- Cardiovascular Center and Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Jun-Bean Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yeonyee E. Yoon
- Cardiovascular Center and Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Yong-Jin Kim
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Goo-Yeong Cho
- Cardiovascular Center and Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
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Alter IL, Chan K, Lechien J, Rameau A. An introduction to machine learning and generative artificial intelligence for otolaryngologists-head and neck surgeons: a narrative review. Eur Arch Otorhinolaryngol 2024; 281:2723-2731. [PMID: 38393353 DOI: 10.1007/s00405-024-08512-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 01/25/2024] [Indexed: 02/25/2024]
Abstract
PURPOSE Despite the robust expansion of research surrounding artificial intelligence (AI) and machine learning (ML) and their applications to medicine, these methodologies often remain opaque and inaccessible to many otolaryngologists. Especially, with the increasing ubiquity of large-language models (LLMs), such as ChatGPT and their potential implementation in clinical practice, clinicians may benefit from a baseline understanding of some aspects of AI. In this narrative review, we seek to clarify underlying concepts, illustrate applications to otolaryngology, and highlight future directions and limitations of these tools. METHODS Recent literature regarding AI principles and otolaryngologic applications of ML and LLMs was reviewed via search in PubMed and Google Scholar. RESULTS Significant recent strides have been made in otolaryngology research utilizing AI and ML, across all subspecialties, including neurotology, head and neck oncology, laryngology, rhinology, and sleep surgery. Potential applications suggested by recent publications include screening and diagnosis, predictive tools, clinical decision support, and clinical workflow improvement via LLMs. Ongoing concerns regarding AI in medicine include ethical concerns around bias and data sharing, as well as the "black box" problem and limitations in explainability. CONCLUSIONS Potential implementations of AI in otolaryngology are rapidly expanding. While implementation in clinical practice remains theoretical for most of these tools, their potential power to influence the practice of otolaryngology is substantial. LEVEL OF EVIDENCE: 4
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Affiliation(s)
- Isaac L Alter
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medical College, 240 E 59 St, New York, NY, 10022, USA
| | - Karly Chan
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medical College, 240 E 59 St, New York, NY, 10022, USA
| | - Jérome Lechien
- Department of Otorhinolaryngology, Head and Neck Surgery, Hôpital Foch, School of Medicine, UFR Simone Veil, Université Versailles Saint-Quentin-en-Yvelines (Paris Saclay University), Paris, France
- Department of Human Anatomy and Experimental Oncology, Faculty of Medicine, UMONS Research Institute for Health and Sciences Technology, University of Mons (UMons), Mons, Belgium
| | - Anaïs Rameau
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medical College, 240 E 59 St, New York, NY, 10022, USA.
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Meinke C, Lueken U, Walter H, Hilbert K. Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders: A systematic review and meta-analysis. Neurosci Biobehav Rev 2024; 160:105640. [PMID: 38548002 DOI: 10.1016/j.neubiorev.2024.105640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 02/29/2024] [Accepted: 03/21/2024] [Indexed: 04/07/2024]
Abstract
Predicting treatment outcome in internalizing mental disorders prior to treatment initiation is pivotal for precision mental healthcare. In this regard, resting-state functional connectivity (rs-FC) and machine learning have often shown promising prediction accuracies. This systematic review and meta-analysis evaluates these studies, considering their risk of bias through the Prediction Model Study Risk of Bias Assessment Tool (PROBAST). We examined the predictive performance of features derived from rs-FC, identified features with the highest predictive value, and assessed the employed machine learning pipelines. We searched the electronic databases Scopus, PubMed and PsycINFO on the 12th of December 2022, which resulted in 13 included studies. The mean balanced accuracy for predicting treatment outcome was 77% (95% CI: [72%- 83%]). rs-FC of the dorsolateral prefrontal cortex had high predictive value in most studies. However, a high risk of bias was identified in all studies, compromising interpretability. Methodological recommendations are provided based on a comprehensive exploration of the studies' machine learning pipelines, and potential fruitful developments are discussed.
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Affiliation(s)
- Charlotte Meinke
- Department of Psychology, Humboldt-Universität zu Berlin, Germany.
| | - Ulrike Lueken
- Department of Psychology, Humboldt-Universität zu Berlin, Germany; German Center for Mental Health (DZPG), partner site Berlin/Potsdam, Germany.
| | - Henrik Walter
- Charité Universtätsmedizin Berlin, corporate member of FU Berlin and Humboldt Universität zu Berlin, Department of Psychiatrie and Psychotherapy, CCM, Germany.
| | - Kevin Hilbert
- Department of Psychology, Humboldt-Universität zu Berlin, Germany; Department of Psychology, Health and Medical University Erfurt, Germany.
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Miladinia M, Zarea K, Gheibizadeh M, Jahangiri M, Karimpourian H, Rokhafroz D. A multiphase study protocol of identifying, and predicting cancer-related symptom clusters: applying a mixed-method design and machine learning algorithms. Front Digit Health 2024; 6:1290689. [PMID: 38707194 PMCID: PMC11066191 DOI: 10.3389/fdgth.2024.1290689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 04/05/2024] [Indexed: 05/07/2024] Open
Abstract
Objectives In recent years, there has been increasing attention on the cluster approach to symptom management. Two significant challenges in the symptom cluster (SC) approach are identifying and predicting these clusters. This multiphase protocol aims to identify SCs in patients with advanced cancer as the primary objective, with the secondary objective of developing machine learning algorithms to predict SCs identified in the first phase. Methods The 2-MIXIP study consists of two main phases. The first phase involves identifying SCs, and the second phase focuses on developing predictive algorithms for the identified SCs. The identification of SCs involves a parallel mixed-method design (quantitative and qualitative). Quantitative and qualitative methods are conducted simultaneously and given equal importance. The data are collected and analyzed independently before being integrated. The quantitative part is conducted using a descriptive-analytical method. The qualitative analysis is conducted using a content analysis approach. Then, the identified SCs from both parts are integrated to determine the final clusters and use them in the second phase. In the second phase, we employ a tree-based machine learning method to create predictive algorithms for SCs using key demographic and clinical patient characteristics. Conclusion The findings of the 2-MIXIP study can help manage cancer patients' symptoms more effectively and enhance clinical decision-making by using SCs prediction. Furthermore, the results of this study can provide guidance for clinical trials aimed at managing symptoms.
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Affiliation(s)
- Mojtaba Miladinia
- Department of Nursing, School of Nursing and Midwifery, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
- Nursing Care Research Center in Chronic Diseases, School of Nursing and Midwifery, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Kourosh Zarea
- Nursing Care Research Center in Chronic Diseases, School of Nursing and Midwifery, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Mahin Gheibizadeh
- Department of Nursing, School of Nursing and Midwifery, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Mina Jahangiri
- Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Hossein Karimpourian
- Department of Medical Oncology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Darioush Rokhafroz
- Department of Nursing, School of Nursing and Midwifery, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
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Tesfaye SH, Seboka BT, Sisay D. Application of machine learning methods for predicting childhood anaemia: Analysis of Ethiopian Demographic Health Survey of 2016. PLoS One 2024; 19:e0300172. [PMID: 38603735 PMCID: PMC11008879 DOI: 10.1371/journal.pone.0300172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 02/22/2024] [Indexed: 04/13/2024] Open
Abstract
Childhood anaemia is a public health problem in Ethiopia. Machine learning (ML) is a growing in medicine field to predict diseases. Diagnosis of childhood anaemia is resource intensive. The aim of this study is to apply machine learning (ML) algorithm to predict childhood anaemia using socio-demographic, economic, and maternal and child related variables. The study used data from 2016 Ethiopian demographic health survey (EDHS). We used Python software version 3.11 to apply and test ML algorithms through logistic regression, Random Forest (RF), Decision Tree, and K-Nearest Neighbours (KNN). We evaluated the performance of each of the ML algorithms using discrimination and calibration parameters. The predictive performance of the algorithms was between 60% and 66%. The logistic regression model was the best predictive model of ML with accuracy (66%), sensitivity (82%), specificity (42%), and AUC (69%), followed by RF with accuracy (64%), sensitivity (79%), specificity (42%), and AUC (63%). The logistic regression and the RF models of ML showed poorest family, child age category between 6 and 23 months, uneducated mother, unemployed mother, and stunting as high importance predictors of childhood anaemia. Applying logistic regression and RF models of ML can detect combinations of predictors of childhood anaemia that can be used in primary health care professionals.
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Affiliation(s)
| | - Binyam Tariku Seboka
- School of Public Health, College of Health Sciences and Medicine, Dilla University, Dilla, Ethiopia
| | - Daniel Sisay
- School of Public Health, College of Health Sciences and Medicine, Dilla University, Dilla, Ethiopia
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Zaylaa AJ, Kourtian S. Advancing Breast Cancer Diagnosis through Breast Mass Images, Machine Learning, and Regression Models. SENSORS (BASEL, SWITZERLAND) 2024; 24:2312. [PMID: 38610522 PMCID: PMC11014206 DOI: 10.3390/s24072312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 03/24/2024] [Accepted: 03/27/2024] [Indexed: 04/14/2024]
Abstract
Breast cancer results from a disruption of certain cells in breast tissue that undergo uncontrolled growth and cell division. These cells most often accumulate and form a lump called a tumor, which may be benign (non-cancerous) or malignant (cancerous). Malignant tumors can spread quickly throughout the body, forming tumors in other areas, which is called metastasis. Standard screening techniques are insufficient in the case of metastasis; therefore, new and advanced techniques based on artificial intelligence (AI), machine learning, and regression models have been introduced, the primary aim of which is to automatically diagnose breast cancer through the use of advanced techniques, classifiers, and real images. Real fine-needle aspiration (FNA) images were collected from Wisconsin, and four classifiers were used, including three machine learning models and one regression model: the support vector machine (SVM), naive Bayes (NB), k-nearest neighbors (k-NN), and decision tree (DT)-C4.5. According to the accuracy, sensitivity, and specificity results, the SVM algorithm had the best performance; it was the most powerful computational classifier with a 97.13% accuracy and 97.5% specificity. It also had around a 96% sensitivity for the diagnosis of breast cancer, unlike the models used for comparison, thereby providing an exact diagnosis on the one hand and a clear classification between benign and malignant tumors on the other hand. As a future research prospect, more algorithms and combinations of features can be considered for the precise, rapid, and effective classification and diagnosis of breast cancer images for imperative decisions.
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Affiliation(s)
- Amira J. Zaylaa
- Biomedical Engineering Program, Electrical and Computer Engineering Department, Faculty of Engineering, Beirut Arab University, Debbieh P.O. Box 11-5020, Lebanon
| | - Sylva Kourtian
- Centre de Recherche du Centre Hospitalier, l’Université de Montréal, Montréal, QC H2X 0A9, Canada;
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Hashemi Gheinani A, Kim J, You S, Adam RM. Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment. Nat Rev Urol 2024; 21:214-242. [PMID: 37604982 DOI: 10.1038/s41585-023-00805-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2023] [Indexed: 08/23/2023]
Abstract
The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.
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Affiliation(s)
- Ali Hashemi Gheinani
- Department of Urology, Boston Children's Hospital, Boston, MA, USA
- Department of Surgery, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Urology, Inselspital, Bern, Switzerland
- Department for BioMedical Research, University of Bern, Bern, Switzerland
| | - Jina Kim
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sungyong You
- Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Rosalyn M Adam
- Department of Urology, Boston Children's Hospital, Boston, MA, USA.
- Department of Surgery, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Yu L, Huang Z, Xiao Z, Tang X, Zeng Z, Tang X, Ouyang W. Unveiling the best predictive models for early‑onset metastatic cancer: Insights and innovations (Review). Oncol Rep 2024; 51:60. [PMID: 38456540 PMCID: PMC10940877 DOI: 10.3892/or.2024.8719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 01/22/2024] [Indexed: 03/09/2024] Open
Abstract
Cancer metastasis is the primary cause of cancer deaths. Metastasis involves the spread of cancer cells from the primary tumors to other body parts, commonly through lymphatic and vascular pathways. Key aspects include the high mutation rate and the capability of metastatic cells to form invasive tumors even without a large initial tumor mass. Particular emphasis is given to early metastasis, occurring in initial cancer stages and often leading to misdiagnosis, which adversely affects survival and prognosis. The present review highlighted the need for improved understanding and detection methods for early metastasis, which has not been effectively identified clinically. The present review demonstrated the clinicopathological and molecular characteristics of early‑onset metastatic types of cancer, noting factors such as age, race, tumor size and location as well as the histological and pathological grade as significant predictors. In conclusion, the present review underscored the importance of early detection and management of metastatic types of cancer and called for improved predictive models, including advanced techniques such as nomograms and machine learning, so as to enhance patient outcomes, acknowledging the challenges and limitations of the current research as well as the necessity for further studies.
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Affiliation(s)
- Liqing Yu
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, P.R. China
- The Second Clinical Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Zhenjun Huang
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, P.R. China
| | - Ziqi Xiao
- The Second Clinical Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Xiaofu Tang
- The Second Clinical Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Ziqiang Zeng
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
- School of Public Health, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Xiaoli Tang
- School of Basic Medicine, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Wenhao Ouyang
- Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510120, P.R. China
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