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Cao S, Hu Y. Interpretable machine learning framework to predict gout associated with dietary fiber and triglyceride-glucose index. Nutr Metab (Lond) 2024; 21:25. [PMID: 38745171 PMCID: PMC11092237 DOI: 10.1186/s12986-024-00802-2] [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: 01/25/2024] [Accepted: 05/01/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Gout prediction is essential for the development of individualized prevention and treatment plans. Our objective was to develop an efficient and interpretable machine learning (ML) model using the SHapley Additive exPlanation (SHAP) to link dietary fiber and triglyceride-glucose (TyG) index to predict gout. METHODS Using datasets from the National Health and Nutrition Examination Survey (NHANES) (2005-2018) population to study dietary fiber, the TyG index was used to predict gout. After evaluating the performance of six ML models and selecting the Light Gradient Boosting Machine (LGBM) as the optimal algorithm, we interpret the LGBM model for predicting gout using SHAP and reveal the decision-making process of the model. RESULTS An initial survey of 70,190 participants was conducted, and after a gradual exclusion process, 12,645 cases were finally included in the study. Selection of the best performing LGBM model for prediction of gout associated with dietary fiber and TyG index (Area under the ROC curve (AUC): 0.823, 95% confidence interval (CI): 0.798-0.848, Accuracy: 95.3%, Brier score: 0.077). The feature importance of SHAP values indicated that age was the most important feature affecting the model output, followed by uric acid (UA). The SHAP values showed that lower dietary fiber values had a more pronounced effect on the positive prediction of the model, while higher values of the TyG index had a more pronounced effect on the positive prediction of the model. CONCLUSION The interpretable LGBM model associated with dietary fiber and TyG index showed high accuracy, efficiency, and robustness in predicting gout. Increasing dietary fiber intake and lowering the TyG index are beneficial in reducing the potential risk of gout.
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Affiliation(s)
- Shunshun Cao
- Pediatric Endocrinology, Genetics and Metabolism, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China
| | - Yangyang Hu
- Reproductive Medicine Center, Obstetrics and Gynecology, The Second Affiliated Hospital, Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
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Liu J, Sun Y, Tian C, Qin D, Gao L. Deciphering cuproptosis-related signatures in pediatric allergic asthma using integrated scRNA-seq and bulk RNA-seq analysis. J Asthma 2024:1-12. [PMID: 38687912 DOI: 10.1080/02770903.2024.2349596] [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: 02/03/2024] [Accepted: 04/25/2024] [Indexed: 05/02/2024]
Abstract
OBJECTIVE Allergic asthma (AA) is common in children. Excess copper is observed in AA patients. It is currently unclear whether copper imbalance can cause cuproptosis in pediatric AA. METHODS The datasets about pediatric AA (GSE40732 and GSE40888) were obtained from Gene Expression Omnibus (GEO) database. The expression of cuproptosis-related genes (CRGs) and immune cell infiltration in pediatric AA samples were analyzed. Single-cell RNA sequencing (scRNA-seq) data (GSE193816) were used to evaluate the expression patterns of CRGs in AA. The identification of differentially expressed genes within clusters was conducted using weighted gene co-expression network analysis. Subsequently, disease progression and cuproptosis-related models were screened using random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and general linear model (GLM) algorithms. RESULTS Four CRGs were notably increased in pediatric AA samples. CD4+ T cells, macrophages and mast cells exhibited a lower cuproptosis score in AA samples, indicating that these immune cells may be closely associated with cuproptosis in AA development. Co-expression network of CRGs in AA was constructed. AA samples were divided into two cuprotosis clusters. Following construction of four machine-learning models, SVM model exhibited the highest efficacy of prediction in the testing set (AUC = 0.952). SVM model containing five important variables can be used for prediction of AA. CONCLUSION This work provided a machine learning model containing five important variables, which may have good diagnostic efficiency for pediatric AA.
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Affiliation(s)
- Jingping Liu
- Nanjing Pukou Hospital of Traditional Chinese Medicine, Nanjing, Jiangsu, China
| | - Yujia Sun
- Nanjing Pukou Hospital of Traditional Chinese Medicine, Nanjing, Jiangsu, China
| | - Chunxin Tian
- Nanjing Pukou Hospital of Traditional Chinese Medicine, Nanjing, Jiangsu, China
| | - Dong Qin
- Nanjing Pukou Hospital of Traditional Chinese Medicine, Nanjing, Jiangsu, China
| | - Lanying Gao
- Nanjing Pukou Hospital of Traditional Chinese Medicine, Nanjing, Jiangsu, China
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3
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Lu J, Yang Y, Hong EK, Yin X, Wang X, Wang Y, Zhang D. Analyzing the structure-activity relationship of raspberry polysaccharides using interpretable artificial neural network model. Int J Biol Macromol 2024; 264:130354. [PMID: 38403223 DOI: 10.1016/j.ijbiomac.2024.130354] [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: 09/12/2023] [Revised: 02/06/2024] [Accepted: 02/19/2024] [Indexed: 02/27/2024]
Abstract
The structure-activity relationship has been a hot topic in the field of polysaccharide research. Six polysaccharides and three polysaccharide fragments were obtained from raspberry pulp. Based on their structural information and immune-enhancing activity data, an artificial neural network (ANN) model was used for prediction, and Gradient-weighted class activation mapping (Grad-CAM) algorithm was exploited for explanation structure-activity relationship of these raspberry polysaccharides in the present study. The structural information and immune activity data of raspberry polysaccharides were respectively used as input and output in the ANN model. The training and testing losses of ANN model was no longer decreased after trained for 200 epochs. The mean-square error (MSE) of training set and test set stabilized around 0.003 and 0.013, and the mean absolute percentage error (MAPE) of training set and test set were 0.21 % and 0.98 %, indicating the trained ANN model converged well and exhibited strong robustness. The interpretability analysis showed that molecular weight, content of arabinose, galactose or galacturonic acid, and glycosyl linkage patterns of →3)-Arap-(1→, Araf-(1→, →4)-Galp-(1 → were the main structural factors greatly affecting the immune-enhancing activity of raspberry polysaccharides. This work may provide a new perspective for the study of structure-activity relationship of polysaccharides.
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Affiliation(s)
- Jie Lu
- School of Ecological and Environmental Engineering, Qinghai University, Xining 810016, China
| | - Yongjing Yang
- School of Ecological and Environmental Engineering, Qinghai University, Xining 810016, China.
| | - Eun-Kyung Hong
- Medvill Co., Ltd. Medvill Research Institute, Seoul 08512, Republic of Korea
| | - Xingxing Yin
- School of Ecological and Environmental Engineering, Qinghai University, Xining 810016, China.
| | - Xuehong Wang
- School of Ecological and Environmental Engineering, Qinghai University, Xining 810016, China
| | - Yuting Wang
- School of Ecological and Environmental Engineering, Qinghai University, Xining 810016, China
| | - Dejun Zhang
- School of Ecological and Environmental Engineering, Qinghai University, Xining 810016, China.
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4
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Aromiwura AA, Settle T, Umer M, Joshi J, Shotwell M, Mattumpuram J, Vorla M, Sztukowska M, Contractor S, Amini A, Kalra DK. Artificial intelligence in cardiac computed tomography. Prog Cardiovasc Dis 2023; 81:54-77. [PMID: 37689230 DOI: 10.1016/j.pcad.2023.09.001] [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: 09/04/2023] [Accepted: 09/04/2023] [Indexed: 09/11/2023]
Abstract
Artificial Intelligence (AI) is a broad discipline of computer science and engineering. Modern application of AI encompasses intelligent models and algorithms for automated data analysis and processing, data generation, and prediction with applications in visual perception, speech understanding, and language translation. AI in healthcare uses machine learning (ML) and other predictive analytical techniques to help sort through vast amounts of data and generate outputs that aid in diagnosis, clinical decision support, workflow automation, and prognostication. Coronary computed tomography angiography (CCTA) is an ideal union for these applications due to vast amounts of data generation and analysis during cardiac segmentation, coronary calcium scoring, plaque quantification, adipose tissue quantification, peri-operative planning, fractional flow reserve quantification, and cardiac event prediction. In the past 5 years, there has been an exponential increase in the number of studies exploring the use of AI for cardiac computed tomography (CT) image acquisition, de-noising, analysis, and prognosis. Beyond image processing, AI has also been applied to improve the imaging workflow in areas such as patient scheduling, urgent result notification, report generation, and report communication. In this review, we discuss algorithms applicable to AI and radiomic analysis; we then present a summary of current and emerging clinical applications of AI in cardiac CT. We conclude with AI's advantages and limitations in this new field.
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Affiliation(s)
| | - Tyler Settle
- Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
| | - Muhammad Umer
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Jonathan Joshi
- Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA
| | - Matthew Shotwell
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Jishanth Mattumpuram
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Mounica Vorla
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Maryta Sztukowska
- Clinical Trials Unit, University of Louisville, Louisville, KY, USA; University of Information Technology and Management, Rzeszow, Poland
| | - Sohail Contractor
- Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA
| | - Amir Amini
- Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA; Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA
| | - Dinesh K Kalra
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA; Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA.
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Huang AA, Huang SY. Technical Report: Machine-Learning Pipeline for Medical Research and Quality-Improvement Initiatives. Cureus 2023; 15:e46549. [PMID: 37933338 PMCID: PMC10625496 DOI: 10.7759/cureus.46549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 10/05/2023] [Indexed: 11/08/2023] Open
Abstract
Machine-learning techniques have been increasing in popularity within medicine during the past decade. However, these computational techniques are not presented in statistical lectures throughout medical school and are perceived to have a high barrier to entry. The objective is to develop a concise pipeline with publicly available data to decrease the learning time towards using machine learning for medical research and quality-improvement initiatives. This report utilized a publicly available machine-learning data package in R (MLDataR) and computational packages (XGBoost) to highlight techniques for machine-learning model development and visualization with SHaply Additive exPlanations (SHAP). A simple six-step process along with example code was constructed to build and visualize machine-learning models. A concrete set of three steps was developed to help with interpretation. Further teaching of these methods could benefit researchers by providing alternative methods for data analysis in medical studies. These could help researchers without computational experience to get a feel for machine learning to better understand the literature and technique.
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Affiliation(s)
- Alexander A Huang
- Surgery, Northwestern University Feinberg School of Medicine, Chicago, USA
| | - Samuel Y Huang
- Internal Medicine, Icahn School of Medicine at Mount Sinai South Nassau, Oceanside, USA
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Li L, Pu C, Jin N, Zhu L, Hu Y, Cascone P, Tao Y, Zhang H. Prediction of 5-year overall survival of tongue cancer based machine learning. BMC Oral Health 2023; 23:567. [PMID: 37574562 PMCID: PMC10423415 DOI: 10.1186/s12903-023-03255-w] [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: 02/01/2023] [Accepted: 07/27/2023] [Indexed: 08/15/2023] Open
Abstract
OBJECTIVE We aimed to develop a 5-year overall survival prediction model for patients with oral tongue squamous cell carcinoma based on machine learning methods. SUBJECTS AND METHODS The data were obtained from electronic medical records of 224 OTSCC patients at the PLA General Hospital. A five-year overall survival prediction model was constructed using logistic regression, Support Vector Machines, Decision Tree, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting Machine. Model performance was evaluated according to the area under the curve (AUC) of the receiver operating characteristic curve. The output of the optimal model was explained using the Python package (SHapley Additive exPlanations, SHAP). RESULTS After passing through the grid search and secondary modeling, the Light Gradient Boosting Machine was the best prediction model (AUC = 0.860). As explained by SHapley Additive exPlanations, N-stage, age, systemic inflammation response index, positive lymph nodes, plasma fibrinogen, lymphocyte-to-monocyte ratio, neutrophil percentage, and T-stage could perform a 5-year overall survival prediction for OTSCC. The 5-year survival rate was 42%. CONCLUSION The Light Gradient Boosting Machine prediction model predicted 5-year overall survival in OTSCC patients, and this predictive tool has potential prognostic implications for patients with OTSCC.
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Affiliation(s)
- Liangbo Li
- Medical School of Chinese PLA, Beijing, China
- Department of Stomatology, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Cheng Pu
- Key Laboratory of Animal Disease and Human Health of Sichuan Province, Chengdu, China
- College of Veterinary Medicine, Sichuan Agricultural University, Sichuan, China
| | - Nenghao Jin
- Medical School of Chinese PLA, Beijing, China
- Department of Stomatology, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Liang Zhu
- Medical School of Chinese PLA, Beijing, China
- Department of Stomatology, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China
| | - Yanchun Hu
- Key Laboratory of Animal Disease and Human Health of Sichuan Province, Chengdu, China
- College of Veterinary Medicine, Sichuan Agricultural University, Sichuan, China
| | - Piero Cascone
- Unicamillus International Meical University, Rome, Italy
| | - Ye Tao
- Department of Stomatology, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China.
| | - Haizhong Zhang
- Department of Stomatology, Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100853, China.
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Mohammadi T, D'Ascenzo F, Pepe M, Bonsignore Zanghì S, Bernardi M, Spadafora L, Frati G, Peruzzi M, De Ferrari GM, Biondi-Zoccai G. Unsupervised Machine Learning with Cluster Analysis in Patients Discharged after an Acute Coronary Syndrome: Insights from a 23,270-Patient Study. Am J Cardiol 2023; 193:44-51. [PMID: 36870114 DOI: 10.1016/j.amjcard.2023.01.048] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 01/06/2023] [Accepted: 01/29/2023] [Indexed: 03/06/2023]
Abstract
Characterization and management of patients admitted for acute coronary syndromes (ACS) remain challenging, and it is unclear whether currently available clinical and procedural features can suffice to inform adequate decision making. We aimed to explore the presence of specific subsets among patients with ACS. The details on patients discharged after ACS were obtained by querying an extensive multicenter registry and detailing patient features, as well as management details. The clinical outcomes included fatal and nonfatal cardiovascular events at 1-year follow-up. After missing data imputation, 2 unsupervised machine learning approaches (k-means and Clustering Large Applications [CLARA]) were used to generate separate clusters with different features. Bivariate- and multivariable-adjusted analyses were performed to compare the different clusters for clinical outcomes. A total of 23,270 patients were included, with 12,930 cases (56%) of ST-elevation myocardial infarction (STEMI). K-means clustering identified 2 main clusters: a first 1 including 21,998 patients (95%) and a second 1 including 1,282 subjects (5%), with equal distribution for STEMI. CLARA generated 2 main clusters: a first 1 including 11,268 patients (48%) and a second 1 with 12,002 subjects (52%). Notably, the STEMI distribution was significantly different in the CLARA-generated clusters. The clinical outcomes were significantly different across clusters, irrespective of the originating algorithm, including death reinfarction and major bleeding, as well as their composite. In conclusion, unsupervised machine learning can be leveraged to explore the patterns in ACS, potentially highlighting specific patient subsets to improve risk stratification and management.
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Affiliation(s)
- Tanya Mohammadi
- School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Fabrizio D'Ascenzo
- Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy
| | - Martino Pepe
- Division of Cardiology, Department of Emergency and Organ Transplantation, University of Bari, Bari, Italy
| | | | - Marco Bernardi
- Department of Clinical, Internal Medicine, Anesthesiology and Cardiovascular Sciences, Sapienza University of Rome, Italy
| | - Luigi Spadafora
- Department of Clinical, Internal Medicine, Anesthesiology and Cardiovascular Sciences, Sapienza University of Rome, Italy
| | - Giacomo Frati
- Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, Latina, Italy; IRCCS NEUROMED, Pozzilli, Italy
| | - Mariangela Peruzzi
- Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, Latina, Italy; Mediterranea Cardiocentro, Napoli, Italy
| | - Gaetano Maria De Ferrari
- Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin, Italy
| | - Giuseppe Biondi-Zoccai
- Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, Latina, Italy; Mediterranea Cardiocentro, Napoli, Italy.
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Testa A, Biondi-Zoccai G, Anticoli S, Pezzella FR, Mangiardi M, DI Giosa A, Marchegiani G, Frati G, Sciarretta S, Perrotta A, Peruzzi M, Cavarretta E, Gaspardone A, Mariano E, Federici M, Montone RA, Dei Giudici A, Versaci B, Versaci F. Cluster analysis of weather and pollution features and its role in predicting acute cardiac or cerebrovascular events. Minerva Med 2022; 113:825-832. [PMID: 35156790 DOI: 10.23736/s0026-4806.22.08036-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
BACKGROUND Despite mounting evidence, the impact of the interplay between weather and pollution features on the risk of acute cardiac and cerebrovascular events has not been entirely appraised. The aim of this study was to perform a comprehensive cluster analysis of weather and pollution features in a large metropolitan area, and their association with acute cardiac and cerebrovascular events. METHODS Anonymized data on acute myocardial infarction (AMI) and acute cerebrovascular events were obtained from 3 tertiary care centers from a single large metropolitan area. Weather and pollution data were obtained averaging measurements from several city measurement stations managed by the competent regional agency for enviromental protection, and from the Metereological Center of Italian Military Aviation. Unsupervised machine learning was performed with hierarchical clustering to identify specific days with distinct weather and pollution features. Clusters were then compared for rates of acute cardiac and cerebrovascular events with Poisson models. RESULTS As expected, significant pairwise correlations were found between weather and pollution features. Building upon these correlations, hierarchical clustering, from a total of 1169 days, generated 4 separate clusters: mostly winter days with low temperatures and high ozone concentrations (cluster 1, N.=60, 5.1%), days with moderately high temperatures and low pollutants concentrations (cluster 2, N.=419, 35.8%), mostly summer and spring days with high temperatures and high ozone concentrations (cluster 3, N.=673, 57.6%), and mostly winter days with low temperatures and low ozone concentrations (cluster 4, N.=17, 1.5%). Overall cluster-wise comparisons showed significant differences in adverse cardiac and cerebrovascular events (P<0.001), as well as in cerebrovascular events (P<0.001) and strokes (P=0.001). Between-cluster comparisons showed that cluster 1 was associated with an increased risk of any event, cerebrovascular events, and strokes in comparison to cluster 2, cluster 3 and cluster 4 (all P<0.05), as well as AMI in comparison to cluster 3 (P=0.047). In addition, cluster 2 was associated with a higher risk of strokes in comparison to cluster 4 (P=0.030). Analysis adjusting for season confirmed the increased risk of any event, cerebrovascular events and strokes for cluster 1 and cluster 2. CONCLUSIONS Unsupervised machine learning can be leveraged to identify specific days with a unique clustering of adverse weather and pollution features which are associated with an increased risk of acute cardiovascular events, especially cerebrovascular events. These findings may improve collective and individual risk prediction and prevention.
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Affiliation(s)
- Alberto Testa
- Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University, Rome, Italy
| | - Giuseppe Biondi-Zoccai
- Mediterranea Cardiocentro, Naples, Italy - .,Scuola Superiore di Study Avanzati, Sapienza University, Rome, Italy
| | | | | | | | | | | | - Giacomo Frati
- Mediterranea Cardiocentro, Naples, Italy.,IRCCS Neuromed, Pozzilli, Isernia, Italy
| | | | | | - Mariangela Peruzzi
- IRCCS Neuromed, Pozzilli, Isernia, Italy.,Department of Clinical, Internal Anestesiology and Cardiovascular Sciences, Sapienza University, Rome, Italy
| | - Elena Cavarretta
- Mediterranea Cardiocentro, Naples, Italy.,Scuola Superiore di Study Avanzati, Sapienza University, Rome, Italy
| | | | - Enrica Mariano
- Department of Systems Medicine, Tor Vergata University, Rome, Italy
| | - Massimo Federici
- Department of Systems Medicine, Tor Vergata University, Rome, Italy
| | - Rocco A Montone
- Department of Cardiovascular Medicine, IRCCS A. Gemelli University Polyclinic Foundation, Rome, Italy
| | - Angela Dei Giudici
- Cardiologic Intensive Care Unit, Hemodynamic and Cardiology, Santa Maria Goretti Hospital, Latina, Italy
| | | | - Francesco Versaci
- Cardiologic Intensive Care Unit, Hemodynamic and Cardiology, Santa Maria Goretti Hospital, Latina, Italy
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Chopannejad S, Sadoughi F, Bagherzadeh R, Shekarchi S. Predicting major adverse cardiovascular events in acute coronary syndrome: A scoping review of machine learning approaches. Appl Clin Inform 2022; 13:720-740. [PMID: 35617971 PMCID: PMC9329142 DOI: 10.1055/a-1863-1589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
Abstract
BACKGROUND Acute coronary syndrome is the topmost cause of death worldwide; therefore, it is necessary to predict major adverse cardiovascular events and cardiovascular deaths in patients with acute coronary syndrome to make correct and timely clinical decisions. OBJECTIVE The current review aimed to highlight algorithms and important predictor variables through examining those studies which used machine learning algorithms for predicting major adverse cardiovascular events in patients with acute coronary syndrome. METHODS In order to predict major adverse cardiovascular events in patients with acute coronary syndrome, the preferred reporting items for scoping reviews guidelines were used. PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases were searched for articles published between 2005 and 2021. The findings of the studies are presented in the form of a narrative synthesis of evidence. RESULTS According to the results, 14 (63.64%) studies did not perform external validation and only used registry data. The algorithms used in this study comprised, inter alia, Regression Logistic, Random Forest, Boosting Ensemble, Non-Boosting Ensemble, Decision Trees, and Naive Bayes. Multiple studies (N=20) achieved a high Area under the ROC Curve between 0.8 to 0.99 in predicting mortality and major adverse cardiovascular events. The predictor variables used in these studies were divided into demographic, clinical, and therapeutic features. However, no study reported the integration of machine learning model into clinical practice. CONCLUSION Machine learning algorithms rendered acceptable results to predict major adverse cardiovascular events and mortality outcomes in patients with acute coronary syndrome. However, these approaches have never been integrated into clinical practice. Further research is required to develop feasible and effective machine learning prediction models to measure their potentially important implications for optimizing the quality of care in patients with acute coronary syndrome.
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Affiliation(s)
- Sara Chopannejad
- Student Research Committee, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
| | - Farahnaz Sadoughi
- School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
| | - Rafat Bagherzadeh
- English Language Department, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
| | - Sakineh Shekarchi
- School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
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