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Rocca A, Brunese MC, Santone A, Varriano G, Viganò L, Caiazzo C, Vallone G, Brunese L, Romano L, Di Serafino M. Radiomics and 256-slice-dual-energy CT in the automated diagnosis of mild acute pancreatitis: the innovation of formal methods and high-resolution CT. LA RADIOLOGIA MEDICA 2024:10.1007/s11547-024-01878-9. [PMID: 39214954 DOI: 10.1007/s11547-024-01878-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Accepted: 08/12/2024] [Indexed: 09/04/2024]
Abstract
INTRODUCTION Acute pancreatitis (AP) is a common disease, and several scores aim to assess its prognosis. Our study aims to automatically recognize mild AP from computed tomography (CT) images in patients with acute abdominal pain but uncertain diagnosis from clinical and serological data through Radiomic model based on formal methods (FMs). METHODS We retrospectively reviewed the CT scans acquired with Dual Source 256-slice CT scanner (Somatom Definition Flash; Siemens Healthineers, Erlangen, Germany) of 80 patients admitted to the radiology unit of Antonio Cardarelli hospital (Naples) with acute abdominal pain. Patients were divided into 2 groups: 40 underwent showed a healthy pancreatic gland, and 40 affected by four different grades (CTSI 0, 1, 2, 3) of mild pancreatitis at CT without clear clinical presentation or biochemical findings. Segmentation was manually performed. Radiologists identified 6 patients with a high expression of diseases (CTSI 3) to formulate a formal property (Rule) to detect AP in the testing set automatically. Once the rule was formulated, and Model Checker classified 70 patients into "healthy" or "unhealthy". RESULTS The model achieved: accuracy 81%, precision 78% and recall 81%. Combining FMs results with radiologists agreement, and applying the mode in clinical practice, the global accuracy would have been 100%. CONCLUSIONS Our model was reliable to automatically detect mild AP at primary diagnosis even in uncertain presentation and it will be tested prospectively in clinical practice.
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Affiliation(s)
- Aldo Rocca
- Department of Medicine and Health Science "V. Tiberio", University of Molise, Campobasso, Italy.
| | - Maria Chiara Brunese
- Department of Medicine and Health Science "V. Tiberio", University of Molise, Campobasso, Italy.
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy.
| | - Antonella Santone
- Department of Medicine and Health Science "V. Tiberio", University of Molise, Campobasso, Italy
| | - Giulia Varriano
- Department of Medicine and Health Science "V. Tiberio", University of Molise, Campobasso, Italy
| | - Luca Viganò
- Hepatobiliary Unit, Department of Minimally Invasive General and Oncologic Surgery, Humanitas Gavazzeni University Hospital, Bergamo, Italy
| | - Corrado Caiazzo
- Department of Medicine and Health Science "V. Tiberio", University of Molise, Campobasso, Italy
| | - Gianfranco Vallone
- Department of Medicine and Health Science "V. Tiberio", University of Molise, Campobasso, Italy
| | - Luca Brunese
- Department of Medicine and Health Science "V. Tiberio", University of Molise, Campobasso, Italy
| | - Luigia Romano
- Department of General and Emergency Radiology, AORN "Antonio Cardarelli", Naples, Italy
| | - Marco Di Serafino
- Department of Medicine and Health Science "V. Tiberio", University of Molise, Campobasso, Italy
- Department of General and Emergency Radiology, AORN "Antonio Cardarelli", Naples, Italy
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Adelson RP, Garikipati A, Zhou Y, Ciobanu M, Tawara K, Barnes G, Singh NP, Mao Q, Das R. Machine Learning Approach with Harmonized Multinational Datasets for Enhanced Prediction of Hypothyroidism in Patients with Type 2 Diabetes. Diagnostics (Basel) 2024; 14:1152. [PMID: 38893680 PMCID: PMC11172278 DOI: 10.3390/diagnostics14111152] [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: 05/03/2024] [Revised: 05/24/2024] [Accepted: 05/29/2024] [Indexed: 06/21/2024] Open
Abstract
Type 2 diabetes (T2D) is a global health concern with increasing prevalence. Comorbid hypothyroidism (HT) exacerbates kidney, cardiac, neurological and other complications of T2D; these risks can be mitigated pharmacologically upon detecting HT. The current HT standard of care (SOC) screening in T2D is infrequent, delaying HT diagnosis and treatment. We present a first-to-date machine learning algorithm (MLA) clinical decision tool to classify patients as low vs. high risk for developing HT comorbid with T2D; the MLA was developed using readily available patient data from harmonized multinational datasets. The MLA was trained on data from NIH All of US (AoU) and UK Biobank (UKBB) (Combined dataset) and achieved a high negative predictive value (NPV) of 0.989 and an AUROC of 0.762 in the Combined dataset, exceeding AUROCs for the models trained on AoU or UKBB alone (0.666 and 0.622, respectively), indicating that increasing dataset diversity for MLA training improves performance. This high-NPV automated tool can supplement SOC screening and rule out T2D patients with low HT risk, allowing for the prioritization of lab-based testing for at-risk patients. Conversely, an MLA output that designates a patient to be at risk of developing HT allows for tailored clinical management and thereby promotes improved patient outcomes.
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Affiliation(s)
| | | | | | | | | | | | | | - Qingqing Mao
- Montera, Inc. dba Forta, 548 Market St, PMB 89605, San Francisco, CA 94104-5401, USA; (R.P.A.); (A.G.); (Y.Z.); (M.C.); (K.T.); (G.B.); (N.P.S.); (R.D.)
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Qian R, Zhuang J, Xie J, Cheng H, Ou H, Lu X, Ouyang Z. Predictive value of machine learning for the severity of acute pancreatitis: A systematic review and meta-analysis. Heliyon 2024; 10:e29603. [PMID: 38655348 PMCID: PMC11035062 DOI: 10.1016/j.heliyon.2024.e29603] [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: 09/18/2023] [Revised: 04/02/2024] [Accepted: 04/10/2024] [Indexed: 04/26/2024] Open
Abstract
Background Predicting the severity of acute pancreatitis (AP) early poses a challenge in clinical practice. While there are well-established clinical scoring tools, their actual predictive performance remains uncertain. Various studies have explored the application of machine-learning methods for early AP prediction. However, a more comprehensive evidence-based assessment is needed to determine their predictive accuracy. Hence, this systematic review and meta-analysis aimed to evaluate the predictive accuracy of machine learning in assessing the severity of AP. Methods PubMed, EMBASE, Cochrane Library, and Web of Science were systematically searched until December 5, 2023. The risk of bias in eligible studies was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Subgroup analyses, based on different machine learning types, were performed. Additionally, the predictive accuracy of mainstream scoring tools was summarized. Results This systematic review ultimately included 33 original studies. The pooled c-index in both the training and validation sets was 0.87 (95 % CI: 0.84-0.89) and 0.88 (95 % CI: 0.86-0.90), respectively. The sensitivity in the training set was 0.81 (95 % CI: 0.77-0.84), and in the validation set, it was 0.79 (95 % CI: 0.71-0.85). The specificity in the training set was 0.84 (95 % CI: 0.78-0.89), and in the validation set, it was 0.90 (95 % CI: 0.86-0.93). The primary model incorporated was logistic regression; however, its predictive accuracy was found to be inferior to that of neural networks, random forests, and xgboost. The pooled c-index of the APACHE II, BISAP, and Ranson were 0.74 (95 % CI: 0.68-0.80), 0.77 (95 % CI: 0.70-0.85), and 0.74 (95 % CI: 0.68-0.79), respectively. Conclusions Machine learning demonstrates excellent accuracy in predicting the severity of AP, providing a reference for updating or developing a straightforward clinical prediction tool.
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Affiliation(s)
- Rui Qian
- Department of Gastroenterology, Shenzhen Bao'an Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518000, China
| | - Jiamei Zhuang
- The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, 518033, China
| | - Jianjun Xie
- Department of Gastroenterology, Shenzhen Bao'an Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518000, China
| | - Honghui Cheng
- Department of Gastroenterology, Shenzhen Bao'an Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518000, China
| | - Haiya Ou
- Department of Gastroenterology, Shenzhen Bao'an Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518000, China
| | - Xiang Lu
- Department of Plumonary and Critical Care Medicine, Shenzhen Bao'an Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518000, China
| | - Zichen Ouyang
- Department of Hepatology, Shenzhen Bao'an Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518000, China
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Yin M, Lin J, Wang Y, Liu Y, Zhang R, Duan W, Zhou Z, Zhu S, Gao J, Liu L, Liu X, Gu C, Huang Z, Xu X, Xu C, Zhu J. Development and validation of a multimodal model in predicting severe acute pancreatitis based on radiomics and deep learning. Int J Med Inform 2024; 184:105341. [PMID: 38290243 DOI: 10.1016/j.ijmedinf.2024.105341] [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: 07/02/2023] [Revised: 12/16/2023] [Accepted: 01/14/2024] [Indexed: 02/01/2024]
Abstract
OBJECTIVE Aim to establish a multimodal model for predicting severe acute pancreatitis (SAP) using machine learning (ML) and deep learning (DL). METHODS In this multicentre retrospective study, patients diagnosed with acute pancreatitis at admission were enrolled from January 2017 to December 2021. Clinical information within 24 h and CT scans within 72 h of admission were collected. First, we trained Model α based on clinical features selected by least absolute shrinkage and selection operator analysis. Second, radiomics features were extracted from 3D-CT scans and Model β was developed on the features after dimensionality reduction using principal component analysis. Third, Model γ was trained on 2D-CT images. Lastly, a multimodal model, namely PrismSAP, was constructed based on aforementioned features in the training set. The predictive accuracy of PrismSAP was verified in the validation and internal test sets and further validated in the external test set. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, recall, precision and F1-score. RESULTS A total of 1,221 eligible patients were randomly split into a training set (n = 864), a validation set (n = 209) and an internal test set (n = 148). Data of 266 patients were for external testing. In the external test set, PrismSAP performed best with the highest AUC of 0.916 (0.873-0.960) among all models [Model α: 0.709 (0.618-0.800); Model β: 0.749 (0.675-0.824); Model γ: 0.687 (0.592-0.782); MCTSI: 0.778 (0.698-0.857); RANSON: 0.642 (0.559-0.725); BISAP: 0.751 (0.668-0.833); SABP: 0.710 (0.621-0.798)]. CONCLUSION The proposed multimodal model outperformed any single-modality models and traditional scoring systems.
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Affiliation(s)
- Minyue Yin
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China
| | - Jiaxi Lin
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China
| | - Yu Wang
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Department of General Surgery, Jintan Hospital Affiliated to Jiangsu University, Changzhou, Jiangsu 213299, China
| | - Yuanjun Liu
- School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
| | - Rufa Zhang
- Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Changshu No. 1 People's Hospital, Suzhou, Jiangsu 215500, China
| | - Wenbin Duan
- Department of Hepatobiliary Surgery, the People's Hospital of Hunan Province, Changsha, Hunan 410002, China
| | - Zhirun Zhou
- Department of Obstetrics and Gynaecology, the Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215004, China
| | - Shiqi Zhu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China
| | - Jingwen Gao
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China
| | - Lu Liu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China
| | - Xiaolin Liu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China
| | - Chenqi Gu
- Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China
| | - Zhou Huang
- Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China
| | - Xiaodan Xu
- Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Changshu No. 1 People's Hospital, Suzhou, Jiangsu 215500, China.
| | - Chunfang Xu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China.
| | - Jinzhou Zhu
- Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215006, China; Suzhou Clinical Centre of Digestive Diseases, Suzhou, Jiangsu 215006, China; Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin 150000, China.
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Ali H, Inayat F, Dhillon R, Patel P, Afzal A, Wilkinson C, Rehman AU, Anwar MS, Nawaz G, Chaudhry A, Awan JR, Afzal MS, Samanta J, Adler DG, Mohan BP. Predicting the risk of early intensive care unit admission for patients hospitalized with acute pancreatitis using supervised machine learning. Proc AMIA Symp 2024; 37:437-447. [PMID: 38628340 PMCID: PMC11018057 DOI: 10.1080/08998280.2024.2326371] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 02/19/2024] [Indexed: 04/19/2024] Open
Abstract
Background Acute pancreatitis (AP) is a complex and life-threatening disease. Early recognition of factors predicting morbidity and mortality is crucial. We aimed to develop and validate a pragmatic model to predict the individualized risk of early intensive care unit (ICU) admission for patients with AP. Methods The 2019 Nationwide Readmission Database was used to identify patients hospitalized with a primary diagnosis of AP without ICU admission. A matched comparison cohort of AP patients with ICU admission within 7 days of hospitalization was identified from the National Inpatient Sample after 1:N propensity score matching. The least absolute shrinkage and selection operator (LASSO) regression was used to select predictors and develop an ICU acute pancreatitis risk (IAPR) score validated by 10-fold cross-validation. Results A total of 1513 patients hospitalized for AP were included. The median age was 50.0 years (interquartile range: 39.0-63.0). The three predictors that were selected included hypoxia (area under the curve [AUC] 0.78), acute kidney injury (AUC 0.72), and cardiac arrhythmia (AUC 0.61). These variables were used to develop a nomogram that displayed excellent discrimination (AUC 0.874) (bootstrap bias-corrected 95% confidence interval 0.824-0.876). There was no evidence of miscalibration (test statistic = 2.88; P = 0.09). For high-risk patients (total score >6 points), the sensitivity was 68.94% and the specificity was 92.66%. Conclusions This supervised machine learning-based model can help recognize high-risk AP hospitalizations. Clinicians may use the IAPR score to identify patients with AP at high risk of ICU admission within the first week of hospitalization.
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Affiliation(s)
- Hassam Ali
- Department of Gastroenterology, East Carolina University Brody School of Medicine, Greenville, North Carolina, USA
| | - Faisal Inayat
- Department of Internal Medicine, Allama Iqbal Medical College, Lahore, Punjab, Pakistan
| | - Rubaid Dhillon
- Department of Gastroenterology, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Pratik Patel
- Department of Gastroenterology, Mather Hospital and Hofstra University Zucker School of Medicine, Port Jefferson, New York, USA
| | - Arslan Afzal
- Department of Gastroenterology, East Carolina University Brody School of Medicine, Greenville, North Carolina, USA
| | - Christin Wilkinson
- Department of Gastroenterology, East Carolina University Brody School of Medicine, Greenville, North Carolina, USA
| | - Attiq Ur Rehman
- Department of Hepatology, Geisinger Wyoming Valley Medical Center, Wilkes-Barre, Pennsylvania, USA
| | - Muhammad Sajeel Anwar
- Department of Internal Medicine, UHS Wilson Medical Center, Johnson City, New York, USA
| | - Gul Nawaz
- Department of Internal Medicine, Allama Iqbal Medical College, Lahore, Punjab, Pakistan
| | | | - Junaid Rasul Awan
- Department of Internal Medicine, Allama Iqbal Medical College, Lahore, Punjab, Pakistan
| | - Muhammad Sohaib Afzal
- Department of Internal Medicine, Louisiana State University Health, Shreveport, Louisiana, USA
| | - Jayanta Samanta
- Department of Gastroenterology, Post Graduate Institute of Medical Research and Education, Chandigarh, Punjab, India
| | - Douglas G. Adler
- Center for Advanced Therapeutic Endoscopy, Porter Adventist Hospital, Centura Health, Denver, Colorado, USA
| | - Babu P. Mohan
- Department of Gastroenterology and Hepatology, University of Utah School of Medicine, Salt Lake City, Utah, USA
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Adelson RP, Garikipati A, Maharjan J, Ciobanu M, Barnes G, Singh NP, Dinenno FA, Mao Q, Das R. Machine Learning Approach for Improved Longitudinal Prediction of Progression from Mild Cognitive Impairment to Alzheimer's Disease. Diagnostics (Basel) 2023; 14:13. [PMID: 38201322 PMCID: PMC10795823 DOI: 10.3390/diagnostics14010013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 12/08/2023] [Accepted: 12/15/2023] [Indexed: 01/12/2024] Open
Abstract
Mild cognitive impairment (MCI) is cognitive decline that can indicate future risk of Alzheimer's disease (AD). We developed and validated a machine learning algorithm (MLA), based on a gradient-boosted tree ensemble method, to analyze phenotypic data for individuals 55-88 years old (n = 493) diagnosed with MCI. Data were analyzed within multiple prediction windows and averaged to predict progression to AD within 24-48 months. The MLA outperformed the mini-mental state examination (MMSE) and three comparison models at all prediction windows on most metrics. Exceptions include sensitivity at 18 months (MLA and MMSE each achieved 0.600); and sensitivity at 30 and 42 months (MMSE marginally better). For all prediction windows, the MLA achieved AUROC ≥ 0.857 and NPV ≥ 0.800. With averaged data for the 24-48-month lookahead timeframe, the MLA outperformed MMSE on all metrics. This study demonstrates that machine learning may provide a more accurate risk assessment than the standard of care. This may facilitate care coordination, decrease healthcare expenditures, and maintain quality of life for patients at risk of progressing from MCI to AD.
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Affiliation(s)
| | | | | | | | | | | | | | - Qingqing Mao
- Montera, Inc. dba Forta, 548 Market St, PMB 89605, San Francisco, CA 94104-5401, USA; (R.P.A.); (A.G.); (J.M.); (M.C.); (G.B.); (N.P.S.); (F.A.D.); (R.D.)
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Ansari MS, Jain D, Budhiraja S. Machine-learning prediction models for any blood component transfusion in hospitalized dengue patients. Hematol Transfus Cell Ther 2023:S2531-1379(23)02584-1. [PMID: 37996385 DOI: 10.1016/j.htct.2023.09.2365] [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: 01/11/2023] [Revised: 04/17/2023] [Accepted: 09/05/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND Blood component transfusions are a common and often necessary medical practice during the epidemics of dengue. Transfusions are required for patients when they developed severe dengue fever or thrombocytopenia of 10×109/L or less. This study therefore investigated the risk factors, performance and effectiveness of eight different machine-learning algorithms to predict blood component transfusion requirements in confirmed dengue cases admitted to hospital. The objective was to study the risk factors that can help to predict blood component transfusion needs. METHODS Eight predictive models were developed based on retrospective data from a private group of hospitals in India. A python package SHAP (SHapley Additive exPlanations) was used to explain the output of the "XGBoost" model. RESULTS Sixteen vital variables were finally selected as having the most significant effects on blood component transfusion prediction. The XGBoost model presented significantly better predictive performance (area under the curve: 0.793; 95 % confidence interval: 0.699-0.795) than the other models. CONCLUSION Predictive modelling techniques can be utilized to streamline blood component preparation procedures and can help in the triage of high-risk patients and readiness of caregivers to provide blood component transfusions when required. This study demonstrates the potential of multilayer algorithms to reasonably predict any blood component transfusion needs which may help healthcare providers make more informed decisions regarding patient care.
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Affiliation(s)
- Md Shahid Ansari
- Department of Clinical Data Analytics, Max Super Speciality Hospital, New Delhi, India
| | - Dinesh Jain
- Department of Clinical Data Analytics, Max Super Speciality Hospital, New Delhi, India.
| | - Sandeep Budhiraja
- Department of Internal Medicine, Max Super Speciality Hospital, New Delhi, India
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Hu JX, Zhao CF, Wang SL, Tu XY, Huang WB, Chen JN, Xie Y, Chen CR. Acute pancreatitis: A review of diagnosis, severity prediction and prognosis assessment from imaging technology, scoring system and artificial intelligence. World J Gastroenterol 2023; 29:5268-5291. [PMID: 37899784 PMCID: PMC10600804 DOI: 10.3748/wjg.v29.i37.5268] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 07/31/2023] [Accepted: 09/14/2023] [Indexed: 09/25/2023] Open
Abstract
Acute pancreatitis (AP) is a potentially life-threatening inflammatory disease of the pancreas, with clinical management determined by the severity of the disease. Diagnosis, severity prediction, and prognosis assessment of AP typically involve the use of imaging technologies, such as computed tomography, magnetic resonance imaging, and ultrasound, and scoring systems, including Ranson, Acute Physiology and Chronic Health Evaluation II, and Bedside Index for Severity in AP scores. Computed tomography is considered the gold standard imaging modality for AP due to its high sensitivity and specificity, while magnetic resonance imaging and ultrasound can provide additional information on biliary obstruction and vascular complications. Scoring systems utilize clinical and laboratory parameters to classify AP patients into mild, moderate, or severe categories, guiding treatment decisions, such as intensive care unit admission, early enteral feeding, and antibiotic use. Despite the central role of imaging technologies and scoring systems in AP management, these methods have limitations in terms of accuracy, reproducibility, practicality and economics. Recent advancements of artificial intelligence (AI) provide new opportunities to enhance their performance by analyzing vast amounts of clinical and imaging data. AI algorithms can analyze large amounts of clinical and imaging data, identify scoring system patterns, and predict the clinical course of disease. AI-based models have shown promising results in predicting the severity and mortality of AP, but further validation and standardization are required before widespread clinical application. In addition, understanding the correlation between these three technologies will aid in developing new methods that can accurately, sensitively, and specifically be used in the diagnosis, severity prediction, and prognosis assessment of AP through complementary advantages.
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Affiliation(s)
- Jian-Xiong Hu
- Intensive Care Unit, The Affiliated Hospital of Putian University, Putian 351100, Fujian Province, China
| | - Cheng-Fei Zhao
- School of Pharmacy and Medical Technology, Putian University, Putian 351100, Fujian Province, China
- Key Laboratory of Pharmaceutical Analysis and Laboratory Medicine, Putian University, Putian 351100, Fujian Province, China
| | - Shu-Ling Wang
- Department of Critical Care Medicine, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, China
| | - Xiao-Yan Tu
- Department of Critical Care Medicine, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, China
| | - Wei-Bin Huang
- Department of Critical Care Medicine, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, China
| | - Jun-Nian Chen
- Department of Critical Care Medicine, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, China
| | - Ying Xie
- School of Mechanical, Electrical and Information Engineering, Putian University, Putian 351100, Fujian Province, China
| | - Cun-Rong Chen
- Department of Critical Care Medicine, Fujian Medical University Union Hospital, Fuzhou 350001, Fujian Province, China
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Li JN, Mu D, Zheng SC, Tian W, Wu ZY, Meng J, Wang RF, Zheng TL, Zhang YL, Windsor J, Lu GT, Wu D. Machine learning improves prediction of severity and outcomes of acute pancreatitis: a prospective multi-center cohort study. SCIENCE CHINA. LIFE SCIENCES 2023; 66:1934-1937. [PMID: 37209250 DOI: 10.1007/s11427-022-2333-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 03/21/2023] [Indexed: 05/22/2023]
Affiliation(s)
- Jia-Ning Li
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Dong Mu
- Department of Gastroenterology, General Hospital of Western Theater Command, Chengdu, 610083, China
| | - Shi-Cheng Zheng
- Department of Gastroenterology, West China Longquan Hospital Sichuan University, Chengdu, 610100, China
| | - Wei Tian
- Department of Gastroenterology, Fangshan Liangxiang Hospital of Beijing, Beijing, 102400, China
| | - Zuo-Yan Wu
- Department of Gastroenterology, The Sixth Hospital of Beijing, Beijing, 100048, China
| | - Jie Meng
- Department of Gastroenterology, Affiliated Hospital of Hebei University, Baoding, 071030, China
| | - Rui-Feng Wang
- Department of Gastroenterology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Tian-Lei Zheng
- Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221004, China
| | - Yue-Lun Zhang
- Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - John Windsor
- Surgical and Translational Research Centre, Faculty of Medical and Health Sciences, University of Auckland, Auckland, Auckland, 1142, New Zealand
| | - Guo-Tao Lu
- Department of Gastroenterology, Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, 225003, China.
| | - Dong Wu
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
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Maharjan J, Garikipati A, Dinenno FA, Ciobanu M, Barnes G, Browning E, DeCurzio J, Mao Q, Das R. Machine learning determination of applied behavioral analysis treatment plan type. Brain Inform 2023; 10:7. [PMID: 36862316 PMCID: PMC9981822 DOI: 10.1186/s40708-023-00186-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 02/06/2023] [Indexed: 03/03/2023] Open
Abstract
BACKGROUND Applied behavioral analysis (ABA) is regarded as the gold standard treatment for autism spectrum disorder (ASD) and has the potential to improve outcomes for patients with ASD. It can be delivered at different intensities, which are classified as comprehensive or focused treatment approaches. Comprehensive ABA targets multiple developmental domains and involves 20-40 h/week of treatment. Focused ABA targets individual behaviors and typically involves 10-20 h/week of treatment. Determining the appropriate treatment intensity involves patient assessment by trained therapists, however, the final determination is highly subjective and lacks a standardized approach. In our study, we examined the ability of a machine learning (ML) prediction model to classify which treatment intensity would be most suited individually for patients with ASD who are undergoing ABA treatment. METHODS Retrospective data from 359 patients diagnosed with ASD were analyzed and included in the training and testing of an ML model for predicting comprehensive or focused treatment for individuals undergoing ABA treatment. Data inputs included demographics, schooling, behavior, skills, and patient goals. A gradient-boosted tree ensemble method, XGBoost, was used to develop the prediction model, which was then compared against a standard of care comparator encompassing features specified by the Behavior Analyst Certification Board treatment guidelines. Prediction model performance was assessed via area under the receiver-operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS The prediction model achieved excellent performance for classifying patients in the comprehensive versus focused treatment groups (AUROC: 0.895; 95% CI 0.811-0.962) and outperformed the standard of care comparator (AUROC 0.767; 95% CI 0.629-0.891). The prediction model also achieved sensitivity of 0.789, specificity of 0.808, PPV of 0.6, and NPV of 0.913. Out of 71 patients whose data were employed to test the prediction model, only 14 misclassifications occurred. A majority of misclassifications (n = 10) indicated comprehensive ABA treatment for patients that had focused ABA treatment as the ground truth, therefore still providing a therapeutic benefit. The three most important features contributing to the model's predictions were bathing ability, age, and hours per week of past ABA treatment. CONCLUSION This research demonstrates that the ML prediction model performs well to classify appropriate ABA treatment plan intensity using readily available patient data. This may aid with standardizing the process for determining appropriate ABA treatments, which can facilitate initiation of the most appropriate treatment intensity for patients with ASD and improve resource allocation.
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Affiliation(s)
- Jenish Maharjan
- Montera Inc. dba Forta, 548 Market St, San Francisco, CA PMB 89605 USA
| | - Anurag Garikipati
- Montera Inc. dba Forta, 548 Market St, San Francisco, CA PMB 89605 USA
| | - Frank A. Dinenno
- Montera Inc. dba Forta, 548 Market St, San Francisco, CA PMB 89605 USA
| | - Madalina Ciobanu
- Montera Inc. dba Forta, 548 Market St, San Francisco, CA PMB 89605 USA
| | - Gina Barnes
- Montera Inc. dba Forta, 548 Market St, San Francisco, CA PMB 89605 USA
| | - Ella Browning
- Montera Inc. dba Forta, 548 Market St, San Francisco, CA PMB 89605 USA
| | - Jenna DeCurzio
- Montera Inc. dba Forta, 548 Market St, San Francisco, CA PMB 89605 USA
| | - Qingqing Mao
- Montera Inc. dba Forta, 548 Market St, San Francisco, CA, PMB 89605, USA.
| | - Ritankar Das
- Montera Inc. dba Forta, 548 Market St, San Francisco, CA PMB 89605 USA
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Early prediction of the severe course, survival, and ICU requirements in acute pancreatitis by artificial intelligence. Pancreatology 2023; 23:176-186. [PMID: 36610872 DOI: 10.1016/j.pan.2022.12.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 11/20/2022] [Accepted: 12/12/2022] [Indexed: 01/09/2023]
Abstract
OBJECTIVE To evaluate the success of artificial intelligence for early prediction of severe course, survival, and intensive care unit(ICU) requirement in patients with acute pancreatitis(AP). METHODS Retrospectively, 1334 patients were included the study. Severity is determined according to the Revised Atlanta Classification(RAC). The success of machine learning(ML) method was evaluated by 13 simple demographic, clinical, etiologic, and laboratory features obtained on ER admission. Additionally, it was evaluated whether Balthazar-computerized tomography severity index(CTSI) at 48-h contributed to success. The dataset was split into two parts, 90% for ML(of which 70% for learning and 30% for testing) and 10% for validation and 5-fold stratified sampling has been utilized. Variable Importance was used in the selection of features during training phase of machine. The Gradient Boost Algorithm trained the machine by KNIME analytics platform. SMOTE has been applied to increase the minority classes for training. The combined effects of the measured features were examined by multivariate logistic regression analysis and reciever operating curve curves of the prediction and confidence of the target variables were obtained. RESULTS Accuracy values for the early estimation of Atlanta severity score, ICU requirement, and survival were found as 88.20%, 98.25%, and 92.77% respectively. When Balthazar-CTSI score is used, results were found as 91.02%, 92.25%, and 98% respectively. CONCLUSIONS The ML method we used successfully predicted the severe course, ICU requirement and survival, with promising accuracy values of over 88%. If 48-h Balthazar-CTSI is included in the calculation, the severity score and survival rates increase even more.
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12
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Application of Machine Learning for Cardiovascular Disease Risk Prediction. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023. [DOI: 10.1155/2023/9418666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
Cardiovascular diseases (CVDs) are a common cause of heart failure globally. The need to explore possible ways to tackle the disease necessitated this study. The study designed a machine learning model for cardiovascular disease risk prediction in accordance with a dataset that contains 11 features which may be used to forecast the disease. The dataset from Kaggle on cardiovascular disease includes approximately 70,000 patient records that were used to determine the outcome. Compared to the UCI dataset, the Kaggle dataset has many more training and validation records. Models created using neural networks, random forests, Bayesian networks, C5.0, and QUEST were compared for this dataset. On training and testing data sets, the results acquired a high accuracy (99.1 percent), which is significantly superior to previous methods. Ahead-of-time detection and diagnosis of cardiac disease, as well as better treatment outcomes, are strong possibilities for the suggested prediction model. Additionally, it may help patients better manage their illness or life forms in order to increase their chances of recovery/survival. The result showed greater accuracy and promising signs that machine-learning algorithms can indeed assist in early identification of the disease and improvement of the treatment outcome.
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Han X, Geng J, Zhang XX, Zhao L, Wang J, Guo WL. Using machine learning models to predict acute pancreatitis in children with pancreaticobiliary maljunction. Surg Today 2023; 53:316-321. [PMID: 35943628 DOI: 10.1007/s00595-022-02571-y] [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: 04/25/2022] [Accepted: 07/02/2022] [Indexed: 11/24/2022]
Abstract
PURPOSE To develop a model to identify risk factors and predictors of acute pancreatitis in children with pancreaticobiliary maljunction (PBM). METHODS We screened consecutive PBM patients treated at two centers between January, 2015 and July, 2021. For machine learning, the cohort was divided randomly at a 6:4 ratio to a training dataset and a validation dataset. Three parallel models were developed using logistic regression (LR), a support vector machine (SVM), and extreme gradient boosting (XGBoost), respectively. Model performance was judged primarily based on the area under the receiver operating curves (AUC). RESULTS A total of 99 patients were included in the analysis, 17 of whom suffered acute pancreatitis and 82 did not. The XGBoost (AUC = 0.814) and SVM (AUC = 0.813) models produced similar performance in the validation dataset; both outperformed the LR model (AUC = 0.805). Based on the SHapley Additive exPlanation values, the most important variable in both the XGBoost and SVM models were age, protein plugs, and white blood cell count. CONCLUSIONS Machine learning models, especially XGBoost and SVM, could be used to predict acute pancreatitis in children with PBM. The most important contributing factor to the models were age, protein plugs, and white blood cell count.
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Affiliation(s)
- Xiao Han
- Department of Radiology, Children's Hospital of Soochow University, Suzhou, 215025, China
| | - Jia Geng
- Clinical Laboratory, 3rd Hospital of Yulin, Yulin, 719000, China
| | - Xin-Xian Zhang
- Department of Radiology, Xuzhou Children's Hospital, Xuzhou, 221002, China
| | - Lian Zhao
- Department of Radiology, Children's Hospital of Soochow University, Suzhou, 215025, China
| | - Jian Wang
- Pediatric Surgery, Children's Hospital of Soochow University, Suzhou, 215025, China
| | - Wan-Liang Guo
- Department of Radiology, Children's Hospital of Soochow University, Suzhou, 215025, China.
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14
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Yuan L, Ji M, Wang S, Wen X, Huang P, Shen L, Xu J. Machine learning model identifies aggressive acute pancreatitis within 48 h of admission: a large retrospective study. BMC Med Inform Decis Mak 2022; 22:312. [PMID: 36447180 PMCID: PMC9707001 DOI: 10.1186/s12911-022-02066-3] [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: 06/28/2022] [Accepted: 11/23/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Acute pancreatitis (AP) with critical illness is linked to increased morbidity and mortality. Current risk scores to identify high-risk AP patients have certain limitations. OBJECTIVE To develop and validate a machine learning tool within 48 h after admission for predicting which patients with AP will develop critical illness based on ubiquitously available clinical, laboratory, and radiologic variables. METHODS 5460 AP patients were enrolled. Clinical, laboratory, and imaging variables were collected within 48 h after hospital admission. Least Absolute Shrinkage Selection Operator with bootstrap method was employed to select the most informative variables. Five different machine learning models were constructed to predictive likelihood of critical illness, and the optimal model (APCU) was selected. External cohort was used to validate APCU. APCU and other risk scores were compared using multivariate analysis. Models were evaluated by area under the curve (AUC). The decision curve analysis was employed to evaluate the standardized net benefit. RESULTS Xgboost was constructed and selected as APCU, involving age, comorbid disease, mental status, pulmonary infiltrates, procalcitonin (PCT), neutrophil percentage (Neu%), ALT/AST, ratio of albumin and globulin, cholinesterase, Urea, Glu, AST and serum total cholesterol. The APCU performed excellently in discriminating AP risk in internal cohort (AUC = 0.95) and external cohort (AUC = 0.873). The APCU was significant for biliogenic AP (OR = 4.25 [2.08-8.72], P < 0.001), alcoholic AP (OR = 3.60 [1.67-7.72], P = 0.001), hyperlipidemic AP (OR = 2.63 [1.28-5.37], P = 0.008) and tumor AP (OR = 4.57 [2.14-9.72], P < 0.001). APCU yielded the highest clinical net benefit, comparatively. CONCLUSION Machine learning tool based on ubiquitously available clinical variables accurately predicts the development of AP, optimizing the management of AP.
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Affiliation(s)
- Lei Yuan
- grid.260478.f0000 0000 9249 2313School of Automation, Nanjing University of Information Science and Technology, Nanjing, China ,grid.412632.00000 0004 1758 2270Department of Information Center, Wuhan University Renmin Hospital, Wuhan, Hubei China ,grid.260478.f0000 0000 9249 2313Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, NanJing, China
| | - Mengyao Ji
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei China
| | - Shuo Wang
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei China
| | - Xinyu Wen
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei China
| | - Pingxiao Huang
- grid.33199.310000 0004 0368 7223Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei China
| | - Lei Shen
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei China
| | - Jun Xu
- grid.260478.f0000 0000 9249 2313School of Automation, Nanjing University of Information Science and Technology, Nanjing, China ,grid.260478.f0000 0000 9249 2313Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, NanJing, China
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15
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Szatmary P, Grammatikopoulos T, Cai W, Huang W, Mukherjee R, Halloran C, Beyer G, Sutton R. Acute Pancreatitis: Diagnosis and Treatment. Drugs 2022; 82:1251-1276. [PMID: 36074322 PMCID: PMC9454414 DOI: 10.1007/s40265-022-01766-4] [Citation(s) in RCA: 111] [Impact Index Per Article: 55.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/04/2022] [Indexed: 11/11/2022]
Abstract
Acute pancreatitis is a common indication for hospital admission, increasing in incidence, including in children, pregnancy and the elderly. Moderately severe acute pancreatitis with fluid and/or necrotic collections causes substantial morbidity, and severe disease with persistent organ failure causes significant mortality. The diagnosis requires two of upper abdominal pain, amylase/lipase ≥ 3 ×upper limit of normal, and/or cross-sectional imaging findings. Gallstones and ethanol predominate while hypertriglyceridaemia and drugs are notable among many causes. Serum triglycerides, full blood count, renal and liver function tests, glucose, calcium, transabdominal ultrasound, and chest imaging are indicated, with abdominal cross-sectional imaging if there is diagnostic uncertainty. Subsequent imaging is undertaken to detect complications, for example, if C-reactive protein exceeds 150 mg/L, or rarer aetiologies. Pancreatic intracellular calcium overload, mitochondrial impairment, and inflammatory responses are critical in pathogenesis, targeted in current treatment trials, which are crucially important as there is no internationally licenced drug to treat acute pancreatitis and prevent complications. Initial priorities are intravenous fluid resuscitation, analgesia, and enteral nutrition, and when necessary, critical care and organ support, parenteral nutrition, antibiotics, pancreatic exocrine and endocrine replacement therapy; all may have adverse effects. Patients with local complications should be referred to specialist tertiary centres to guide further management, which may include drainage and/or necrosectomy. The impact of acute pancreatitis can be devastating, so prevention or reduction of the risk of recurrence and progression to chronic pancreatitis with an increased risk of pancreas cancer requires proactive management that should be long term for some patients.
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Affiliation(s)
- Peter Szatmary
- Liverpool Pancreatitis Research Group, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.,Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.,Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Tassos Grammatikopoulos
- Paediatric Liver, GI and Nutrition Centre, King's College Hospital NHS Foundation Trust, London, UK
| | - Wenhao Cai
- Liverpool Pancreatitis Research Group, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.,Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.,West China Centre of Excellence for Pancreatitis and West China-Liverpool Biomedical Research Centre, West China Hospital, Sichuan University, Chengdu, China
| | - Wei Huang
- West China Centre of Excellence for Pancreatitis and West China-Liverpool Biomedical Research Centre, West China Hospital, Sichuan University, Chengdu, China
| | - Rajarshi Mukherjee
- Liverpool Pancreatitis Research Group, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.,Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK.,Department of Molecular Physiology and Cell Signalling, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool , UK
| | - Chris Halloran
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.,Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Georg Beyer
- Department of Medicine II, University Hospital, LMU Munich, Munich, Germany
| | - Robert Sutton
- Liverpool Pancreatitis Research Group, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK. .,Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK. .,Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK.
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Laterre PF, Collienne C. Improving the management of severe acute pancreatitis: The new guidelines from the French Society of Anaesthesia and Intensive Care Medicine. Anaesth Crit Care Pain Med 2022; 41:101103. [PMID: 35715021 DOI: 10.1016/j.accpm.2022.101103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Pierre-François Laterre
- Department of Critical Care Medicine, St Luc University Hospital, UCLouvain, Brussels, Belgium.
| | - Christine Collienne
- Department of Critical Care Medicine, St Luc University Hospital, UCLouvain, Brussels, Belgium
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Hong W, Lu Y, Zhou X, Jin S, Pan J, Lin Q, Yang S, Basharat Z, Zippi M, Goyal H. Usefulness of Random Forest Algorithm in Predicting Severe Acute Pancreatitis. Front Cell Infect Microbiol 2022; 12:893294. [PMID: 35755843 PMCID: PMC9226542 DOI: 10.3389/fcimb.2022.893294] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 04/29/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND AIMS This study aimed to develop an interpretable random forest model for predicting severe acute pancreatitis (SAP). METHODS Clinical and laboratory data of 648 patients with acute pancreatitis were retrospectively reviewed and randomly assigned to the training set and test set in a 3:1 ratio. Univariate analysis was used to select candidate predictors for the SAP. Random forest (RF) and logistic regression (LR) models were developed on the training sample. The prediction models were then applied to the test sample. The performance of the risk models was measured by calculating the area under the receiver operating characteristic (ROC) curves (AUC) and area under precision recall curve. We provide visualized interpretation by using local interpretable model-agnostic explanations (LIME). RESULTS The LR model was developed to predict SAP as the following function: -1.10-0.13×albumin (g/L) + 0.016 × serum creatinine (μmol/L) + 0.14 × glucose (mmol/L) + 1.63 × pleural effusion (0/1)(No/Yes). The coefficients of this formula were utilized to build a nomogram. The RF model consists of 16 variables identified by univariate analysis. It was developed and validated by a tenfold cross-validation on the training sample. Variables importance analysis suggested that blood urea nitrogen, serum creatinine, albumin, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, calcium, and glucose were the most important seven predictors of SAP. The AUCs of RF model in tenfold cross-validation of the training set and the test set was 0.89 and 0.96, respectively. Both the area under precision recall curve and the diagnostic accuracy of the RF model were higher than that of both the LR model and the BISAP score. LIME plots were used to explain individualized prediction of the RF model. CONCLUSIONS An interpretable RF model exhibited the highest discriminatory performance in predicting SAP. Interpretation with LIME plots could be useful for individualized prediction in a clinical setting. A nomogram consisting of albumin, serum creatinine, glucose, and pleural effusion was useful for prediction of SAP.
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Affiliation(s)
- Wandong Hong
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- *Correspondence: Wandong Hong,
| | - Yajing Lu
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaoying Zhou
- School of the First Clinical Medical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Shengchun Jin
- School of the First Clinical Medical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Jingyi Pan
- School of the First Clinical Medical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Qingyi Lin
- School of the First Clinical Medical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Shaopeng Yang
- School of the First Clinical Medical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Zarrin Basharat
- Jamil-ur-Rahman Center for Genome Research, Dr. Panjwani Centre for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan
| | - Maddalena Zippi
- Unit of Gastroenterology and Digestive Endoscopy, Sandro Pertini Hospital, Rome, Italy
| | - Hemant Goyal
- Department of Medicine, The Wright Center for Graduate Medical Education, Scranton, PA, United States
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Thapa R, Garikipati A, Shokouhi S, Hurtado M, Barnes G, Hoffman J, Calvert J, Katzmann L, Mao Q, Das R. Usability of Electronic Health records in Predicting Short-term falls: Machine learning Applications in Senior Care Facilities (Preprint). JMIR Aging 2021; 5:e35373. [PMID: 35363146 PMCID: PMC9015781 DOI: 10.2196/35373] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/16/2022] [Accepted: 02/07/2022] [Indexed: 11/23/2022] Open
Abstract
Background Short-term fall prediction models that use electronic health records (EHRs) may enable the implementation of dynamic care practices that specifically address changes in individualized fall risk within senior care facilities. Objective The aim of this study is to implement machine learning (ML) algorithms that use EHR data to predict a 3-month fall risk in residents from a variety of senior care facilities providing different levels of care. Methods This retrospective study obtained EHR data (2007-2021) from Juniper Communities’ proprietary database of 2785 individuals primarily residing in skilled nursing facilities, independent living facilities, and assisted living facilities across the United States. We assessed the performance of 3 ML-based fall prediction models and the Juniper Communities’ fall risk assessment. Additional analyses were conducted to examine how changes in the input features, training data sets, and prediction windows affected the performance of these models. Results The Extreme Gradient Boosting model exhibited the highest performance, with an area under the receiver operating characteristic curve of 0.846 (95% CI 0.794-0.894), specificity of 0.848, diagnostic odds ratio of 13.40, and sensitivity of 0.706, while achieving the best trade-off in balancing true positive and negative rates. The number of active medications was the most significant feature associated with fall risk, followed by a resident’s number of active diseases and several variables associated with vital signs, including diastolic blood pressure and changes in weight and respiratory rates. The combination of vital signs with traditional risk factors as input features achieved higher prediction accuracy than using either group of features alone. Conclusions This study shows that the Extreme Gradient Boosting technique can use a large number of features from EHR data to make short-term fall predictions with a better performance than that of conventional fall risk assessments and other ML models. The integration of routinely collected EHR data, particularly vital signs, into fall prediction models may generate more accurate fall risk surveillance than models without vital signs. Our data support the use of ML models for dynamic, cost-effective, and automated fall predictions in different types of senior care facilities.
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