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Hosseini K, Behnoush AH, Khalaji A, Etemadi A, Soleimani H, Pasebani Y, Jenab Y, Masoudkabir F, Tajdini M, Mehrani M, Nanna MG. Machine learning prediction of one-year mortality after percutaneous coronary intervention in acute coronary syndrome patients. Int J Cardiol 2024; 409:132191. [PMID: 38777044 DOI: 10.1016/j.ijcard.2024.132191] [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: 03/10/2024] [Revised: 04/01/2024] [Accepted: 05/17/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND Machine learning (ML) models have the potential to accurately predict outcomes and offer novel insights into inter-variable correlations. In this study, we aimed to design ML models for the prediction of 1-year mortality after percutaneous coronary intervention (PCI) in patients with acute coronary syndrome. METHODS This study was performed on 13,682 patients at Tehran Heart Center from 2015 to 2021. Patients were split into 70:30 for testing and training. Four ML models were designed: a traditional Logistic Regression (LR) model, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Ada Boost models. The importance of features was calculated using the RF feature selector and SHAP based on the XGBoost model. The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) for the prediction on the testing dataset was the main measure of the model's performance. RESULTS From a total of 9,073 patients with >1-year follow-up, 340 participants died. Higher age and higher rates of comorbidities were observed in these patients. Body mass index and lipid profile demonstrated a U-shaped correlation with the outcome. Among the models, RF had the best discrimination (AUC 0.866), while the highest sensitivity (80.9%) and specificity (88.3%) were for LR and XGBoost models, respectively. All models had AUCs of >0.8. CONCLUSION ML models can predict 1-year mortality after PCI with high performance. A classic LR statistical approach showed comparable results with other ML models. The individual-level assessment of inter-variable correlations provided new insights into the non-linear contribution of risk factors to post-PCI mortality.
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Affiliation(s)
- Kaveh Hosseini
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Hossein Behnoush
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; School of Medicine, Tehran University of Medical Sciences, Tehran, Iran; Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
| | - Amirmohammad Khalaji
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; School of Medicine, Tehran University of Medical Sciences, Tehran, Iran; Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Etemadi
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamidreza Soleimani
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Yeganeh Pasebani
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Yaser Jenab
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Farzad Masoudkabir
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Masih Tajdini
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Mehrani
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Michael G Nanna
- Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT, USA
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Kasim S, Amir Rudin PNF, Malek S, Ibrahim KS, Wan Ahmad WA, Fong AYY, Lin WY, Aziz F, Ibrahim N. Ensemble machine learning for predicting in-hospital mortality in Asian women with ST-elevation myocardial infarction (STEMI). Sci Rep 2024; 14:12378. [PMID: 38811643 PMCID: PMC11137033 DOI: 10.1038/s41598-024-61151-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: 02/21/2023] [Accepted: 05/02/2024] [Indexed: 05/31/2024] Open
Abstract
The accurate prediction of in-hospital mortality in Asian women after ST-Elevation Myocardial Infarction (STEMI) remains a crucial issue in medical research. Existing models frequently neglect this demographic's particular attributes, resulting in poor treatment outcomes. This study aims to improve the prediction of in-hospital mortality in multi-ethnic Asian women with STEMI by employing both base and ensemble machine learning (ML) models. We centred on the development of demographic-specific models using data from the Malaysian National Cardiovascular Disease Database spanning 2006 to 2016. Through a careful iterative feature selection approach that included feature importance and sequential backward elimination, significant variables such as systolic blood pressure, Killip class, fasting blood glucose, beta-blockers, angiotensin-converting enzyme inhibitors (ACE), and oral hypoglycemic medications were identified. The findings of our study revealed that ML models with selected features outperformed the conventional Thrombolysis in Myocardial Infarction (TIMI) Risk score, with area under the curve (AUC) ranging from 0.60 to 0.93 versus TIMI's AUC of 0.81. Remarkably, our best-performing ensemble ML model was surpassed by the base ML model, support vector machine (SVM) Linear with SVM selected features (AUC: 0.93, CI: 0.89-0.98 versus AUC: 0.91, CI: 0.87-0.96). Furthermore, the women-specific model outperformed a non-gender-specific STEMI model (AUC: 0.92, CI: 0.87-0.97). Our findings demonstrate the value of women-specific ML models over standard approaches, emphasizing the importance of continued testing and validation to improve clinical care for women with STEMI.
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Affiliation(s)
- Sazzli Kasim
- Cardiology Department, Faculty of Medicine, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
- Cardiac Vascular and Lung Research Institute, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
- National Heart Association of Malaysia, Heart House, Kuala Lumpur, Malaysia
- Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh Campus, Sungai Buloh, Malaysia
| | | | - Sorayya Malek
- Institute of Biological Sciences, Faculty of Science, University Malaya, Kuala Lumpur, Malaysia.
| | - Khairul Shafiq Ibrahim
- Cardiology Department, Faculty of Medicine, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
- Cardiac Vascular and Lung Research Institute, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
- National Heart Association of Malaysia, Heart House, Kuala Lumpur, Malaysia
| | - Wan Azman Wan Ahmad
- National Heart Association of Malaysia, Heart House, Kuala Lumpur, Malaysia
- Division of Cardiology, University Malaya Medical Centre (UMMC), Kuala Lumpur, Malaysia
| | - Alan Yean Yip Fong
- National Heart Association of Malaysia, Heart House, Kuala Lumpur, Malaysia
- Department of Cardiology, Sarawak General Hospital, Kuching, Sarawak, Malaysia
| | - Wan Yin Lin
- Institute of Biological Sciences, Faculty of Science, University Malaya, Kuala Lumpur, Malaysia
| | - Firdaus Aziz
- School of Liberal Studies, Universiti Kebangsaan Malaysia, Bangi, Malaysia
| | - Nurulain Ibrahim
- Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh Campus, Sungai Buloh, Malaysia
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Jeng SL, Tu MJ, Lin CW, Lin JJ, Tseng HH, Jang FL, Lu MK, Chen PS, Huang CC, Chang WH, Tan HP, Lin SH. Machine learning for prediction of schizophrenia based on identifying the primary and interaction effects of minor physical anomalies. J Psychiatr Res 2024; 172:108-118. [PMID: 38373372 DOI: 10.1016/j.jpsychires.2024.02.032] [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: 11/07/2023] [Revised: 02/08/2024] [Accepted: 02/13/2024] [Indexed: 02/21/2024]
Abstract
In the neurodevelopmental model of schizophrenia, minor physical anomalies (MPAs) are considered neurodevelopmental markers of schizophrenia. To date, there has been no research to evaluate the interaction between MPAs. Our study built and used a machine learning model to predict the risk of schizophrenia based on measurements of MPA items and to investigate the potential primary and interaction effects of MPAs. The study included 470 patients with schizophrenia and 354 healthy controls. The models used are classical statistical model, Logistic Regression (LR), and machine leaning models, Decision Tree (DT) and Random Forest (RF). We also plotted two-dimensional scatter diagrams and three-dimensional linear/quadratic discriminant analysis (LDA/QDA) graphs for comparison with the DT dendritic structure. We found that RF had the highest predictive power for schizophrenia (Full-training AUC = 0.97 and 5-fold cross-validation AUC = 0.75). We identified several primary MPAs, such as the mouth region, high palate, furrowed tongue, skull height and mouth width. Quantitative MPA analysis indicated that the higher skull height and the narrower mouth width, the higher the risk of schizophrenia. In the interaction, we further identified that skull height and mouth width, furrowed tongue and skull height, high palate and skull height, and high palate and furrowed tongue, showed significant two-item interactions with schizophrenia. A weak three-item interaction was found between high palate, skull height, and mouth width. In conclusion, we found that the two machine learning methods showed good predictive ability in assessing the risk of schizophrenia using the primary and interaction effects of MPAs.
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Affiliation(s)
- Shuen-Lin Jeng
- Department of Statistics, Institute of Data Science, and Center for Innovative FinTech Business Models, National Cheng Kung University, Tainan, Taiwan
| | - Ming-Jun Tu
- Department of Statistics, National Cheng Kung University, Tainan, Taiwan
| | - Chih-Wei Lin
- Department of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Jin-Jia Lin
- Department of Psychiatry, Chi Mei Medical Center, Tainan, Taiwan
| | - Huai-Hsuan Tseng
- Department of Psychiatry, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Fong-Lin Jang
- Department of Psychiatry, Chi Mei Medical Center, Tainan, Taiwan
| | - Ming-Kun Lu
- Jianan Psychiatric Center, Ministry of Health and Welfare, Tainan, Taiwan
| | - Po-See Chen
- Department of Psychiatry, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Chih-Chun Huang
- Department of Psychiatry, National Cheng Kung University Hospital, Dou-Liou Branch, Yunlin, Taiwan
| | - Wei-Hung Chang
- Department of Psychiatry, National Cheng Kung University Hospital, Dou-Liou Branch, Yunlin, Taiwan
| | - Hung-Pin Tan
- Jianan Psychiatric Center, Ministry of Health and Welfare, Tainan, Taiwan
| | - Sheng-Hsiang Lin
- Department of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Institute of Clinical Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan; Biostatistics Consulting Center, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
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Razavi SR, Szun T, Zaremba AC, Shah AH, Moussavi Z. 1-Year Mortality Prediction through Artificial Intelligence Using Hemodynamic Trace Analysis among Patients with ST Elevation Myocardial Infarction. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:558. [PMID: 38674204 PMCID: PMC11052412 DOI: 10.3390/medicina60040558] [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: 03/02/2024] [Revised: 03/23/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024]
Abstract
Background and Objectives: Patients presenting with ST Elevation Myocardial Infarction (STEMI) due to occlusive coronary arteries remain at a higher risk of excess morbidity and mortality despite being treated with primary percutaneous coronary intervention (PPCI). Identifying high-risk patients is prudent so that close monitoring and timely interventions can improve outcomes. Materials and Methods: A cohort of 605 STEMI patients [64.2 ± 13.2 years, 432 (71.41%) males] treated with PPCI were recruited. Their arterial pressure (AP) wave recorded throughout the PPCI procedure was analyzed to extract features to predict 1-year mortality. After denoising and extracting features, we developed two distinct feature selection strategies. The first strategy uses linear discriminant analysis (LDA), and the second employs principal component analysis (PCA), with each method selecting the top five features. Then, three machine learning algorithms were employed: LDA, K-nearest neighbor (KNN), and support vector machine (SVM). Results: The performance of these algorithms, measured by the area under the curve (AUC), ranged from 0.73 to 0.77, with accuracy, specificity, and sensitivity ranging between 68% and 73%. Moreover, we extended the analysis by incorporating demographics, risk factors, and catheterization information. This significantly improved the overall accuracy and specificity to more than 76% while maintaining the same level of sensitivity. This resulted in an AUC greater than 0.80 for most models. Conclusions: Machine learning algorithms analyzing hemodynamic traces in STEMI patients identify high-risk patients at risk of mortality.
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Affiliation(s)
- Seyed Reza Razavi
- Biomedical Engineering Program, University of Manitoba, Winnipeg, MB R3T 5V6, Canada;
| | - Tyler Szun
- Department of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 3P5, Canada; (T.S.); (A.C.Z.); (A.H.S.)
| | - Alexander C. Zaremba
- Department of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 3P5, Canada; (T.S.); (A.C.Z.); (A.H.S.)
| | - Ashish H. Shah
- Department of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 3P5, Canada; (T.S.); (A.C.Z.); (A.H.S.)
| | - Zahra Moussavi
- Biomedical Engineering Program, University of Manitoba, Winnipeg, MB R3T 5V6, Canada;
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Zheng H, Sherazi SWA, Lee JY. A cost-sensitive deep neural network-based prediction model for the mortality in acute myocardial infarction patients with hypertension on imbalanced data. Front Cardiovasc Med 2024; 11:1276608. [PMID: 38566962 PMCID: PMC10986180 DOI: 10.3389/fcvm.2024.1276608] [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: 09/12/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Background and objectives Hypertension is one of the most serious risk factors and the leading cause of mortality in patients with cardiovascular diseases (CVDs). It is necessary to accurately predict the mortality of patients suffering from CVDs with hypertension. Therefore, this paper proposes a novel cost-sensitive deep neural network (CSDNN)-based mortality prediction model for out-of-hospital acute myocardial infarction (AMI) patients with hypertension on imbalanced data. Methods The synopsis of our research is as follows. First, the experimental data is extracted from the Korea Acute Myocardial Infarction Registry-National Institutes of Health (KAMIR-NIH) and preprocessed with several approaches. Then the imbalanced experimental dataset is divided into training data (80%) and test data (20%). After that, we design the proposed CSDNN-based mortality prediction model, which can solve the skewed class distribution between the majority and minority classes in the training data. The threshold moving technique is also employed to enhance the performance of the proposed model. Finally, we evaluate the performance of the proposed model using the test data and compare it with other commonly used machine learning (ML) and data sampling-based ensemble models. Moreover, the hyperparameters of all models are optimized through random search strategies with a 5-fold cross-validation approach. Results and discussion In the result, the proposed CSDNN model with the threshold moving technique yielded the best results on imbalanced data. Additionally, our proposed model outperformed the best ML model and the classic data sampling-based ensemble model with an AUC of 2.58% and 2.55% improvement, respectively. It aids in decision-making and offers a precise mortality prediction for AMI patients with hypertension.
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Affiliation(s)
- Huilin Zheng
- Department of Computer Science, Chungbuk National University, Cheongju, Republic of Korea
- College of Computer Science and Engineering, Guilin University of Technology, Guilin, China
| | | | - Jong Yun Lee
- Department of Computer Science, Chungbuk National University, Cheongju, Republic of Korea
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Kasim S, Amir Rudin PNF, Malek S, Aziz F, Wan Ahmad WA, Ibrahim KS, Muhmad Hamidi MH, Raja Shariff RE, Fong AYY, Song C. Data analytics approach for short- and long-term mortality prediction following acute non-ST-elevation myocardial infarction (NSTEMI) and Unstable Angina (UA) in Asians. PLoS One 2024; 19:e0298036. [PMID: 38358964 PMCID: PMC10868757 DOI: 10.1371/journal.pone.0298036] [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: 11/11/2023] [Accepted: 01/17/2024] [Indexed: 02/17/2024] Open
Abstract
BACKGROUND Traditional risk assessment tools often lack accuracy when predicting the short- and long-term mortality following a non-ST-segment elevation myocardial infarction (NSTEMI) or Unstable Angina (UA) in specific population. OBJECTIVE To employ machine learning (ML) and stacked ensemble learning (EL) methods in predicting short- and long-term mortality in Asian patients diagnosed with NSTEMI/UA and to identify the associated features, subsequently evaluating these findings against established risk scores. METHODS We analyzed data from the National Cardiovascular Disease Database for Malaysia (2006-2019), representing a diverse NSTEMI/UA Asian cohort. Algorithm development utilized in-hospital records of 9,518 patients, 30-day data from 7,133 patients, and 1-year data from 7,031 patients. This study utilized 39 features, including demographic, cardiovascular risk, medication, and clinical features. In the development of the stacked EL model, four base learner algorithms were employed: eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), with the Generalized Linear Model (GLM) serving as the meta learner. Significant features were chosen and ranked using ML feature importance with backward elimination. The predictive performance of the algorithms was assessed using the area under the curve (AUC) as a metric. Validation of the algorithms was conducted against the TIMI for NSTEMI/UA using a separate validation dataset, and the net reclassification index (NRI) was subsequently determined. RESULTS Using both complete and reduced features, the algorithm performance achieved an AUC ranging from 0.73 to 0.89. The top-performing ML algorithm consistently surpassed the TIMI risk score for in-hospital, 30-day, and 1-year predictions (with AUC values of 0.88, 0.88, and 0.81, respectively, all p < 0.001), while the TIMI scores registered significantly lower at 0.55, 0.54, and 0.61. This suggests the TIMI score tends to underestimate patient mortality risk. The net reclassification index (NRI) of the best ML algorithm for NSTEMI/UA patients across these periods yielded an NRI between 40-60% (p < 0.001) relative to the TIMI NSTEMI/UA risk score. Key features identified for both short- and long-term mortality included age, Killip class, heart rate, and Low-Molecular-Weight Heparin (LMWH) administration. CONCLUSIONS In a broad multi-ethnic population, ML approaches outperformed conventional TIMI scoring in classifying patients with NSTEMI and UA. ML allows for the precise identification of unique characteristics within individual Asian populations, improving the accuracy of mortality predictions. Continuous development, testing, and validation of these ML algorithms holds the promise of enhanced risk stratification, thereby revolutionizing future management strategies and patient outcomes.
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Affiliation(s)
- Sazzli Kasim
- Cardiology Department, Faculty of Medicine, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
- Cardiac Vascular and Lung Research Institute, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
- National Heart Association of Malaysia, Heart House, Kuala Lumpur, Malaysia
| | | | - Sorayya Malek
- Faculty of Science, Institute of Biological Sciences, University Malaya, Kuala Lumpur, Malaysia
| | - Firdaus Aziz
- School of Liberal Studies, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
| | - Wan Azman Wan Ahmad
- National Heart Association of Malaysia, Heart House, Kuala Lumpur, Malaysia
- Division of Cardiology, University Malaya Medical Centre (UMMC), Kuala Lumpur, Malaysia
| | - Khairul Shafiq Ibrahim
- Cardiology Department, Faculty of Medicine, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
- Cardiac Vascular and Lung Research Institute, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
- National Heart Association of Malaysia, Heart House, Kuala Lumpur, Malaysia
| | - Muhammad Hanis Muhmad Hamidi
- Cardiology Department, Faculty of Medicine, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
- Cardiac Vascular and Lung Research Institute, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
- National Heart Association of Malaysia, Heart House, Kuala Lumpur, Malaysia
| | - Raja Ezman Raja Shariff
- Cardiology Department, Faculty of Medicine, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
- Cardiac Vascular and Lung Research Institute, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
- National Heart Association of Malaysia, Heart House, Kuala Lumpur, Malaysia
| | - Alan Yean Yip Fong
- National Heart Association of Malaysia, Heart House, Kuala Lumpur, Malaysia
- Department of Cardiology, Sarawak General Hospital, Kuching, Sarawak, Malaysia
| | - Cheen Song
- Faculty of Science, Institute of Biological Sciences, University Malaya, Kuala Lumpur, Malaysia
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Alkhamis MA, Al Jarallah M, Attur S, Zubaid M. Interpretable machine learning models for predicting in-hospital and 30 days adverse events in acute coronary syndrome patients in Kuwait. Sci Rep 2024; 14:1243. [PMID: 38216605 PMCID: PMC10786865 DOI: 10.1038/s41598-024-51604-8] [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: 11/14/2023] [Accepted: 01/07/2024] [Indexed: 01/14/2024] Open
Abstract
The relationships between acute coronary syndromes (ACS) adverse events and the associated risk factors are typically complicated and nonlinear, which poses significant challenges to clinicians' attempts at risk stratification. Here, we aim to explore the implementation of modern risk stratification tools to untangle how these complex factors shape the risk of adverse events in patients with ACS. We used an interpretable multi-algorithm machine learning (ML) approach and clinical features to fit predictive models to 1,976 patients with ACS in Kuwait. We demonstrated that random forest (RF) and extreme gradient boosting (XGB) algorithms, remarkably outperform traditional logistic regression model (AUCs = 0.84 & 0.79 for RF and XGB, respectively). Our in-hospital adverse events model identified left ventricular ejection fraction as the most important predictor with the highest interaction strength with other factors. However, using the 30-days adverse events model, we found that performing an urgent coronary artery bypass graft was the most important predictor, with creatinine levels having the strongest overall interaction with other related factors. Our ML models not only untangled the non-linear relationships that shape the clinical epidemiology of ACS adverse events but also elucidated their risk in individual patients based on their unique features.
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Affiliation(s)
- Moh A Alkhamis
- Department of Epidemiology and Biostatistics, Health Sciences Center, College of Public Health, Kuwait University, Kuwait City, Kuwait.
| | - Mohammad Al Jarallah
- Department of Cardiology, Sabah Al Ahmed Cardiac Center, Ministry of Health, Kuwait City, Kuwait
| | - Sreeja Attur
- Department of Medicine, Health Sciences Center, Faculty of Medicine, Kuwait University, Kuwait City, Kuwait
| | - Mohammad Zubaid
- Department of Medicine, Health Sciences Center, Faculty of Medicine, Kuwait University, Kuwait City, Kuwait
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Zhang X, Wang X, Xu L, Liu J, Ren P, Wu H. The predictive value of machine learning for mortality risk in patients with acute coronary syndromes: a systematic review and meta-analysis. Eur J Med Res 2023; 28:451. [PMID: 37864271 PMCID: PMC10588162 DOI: 10.1186/s40001-023-01027-4] [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: 01/12/2023] [Accepted: 01/20/2023] [Indexed: 10/22/2023] Open
Abstract
BACKGROUND Acute coronary syndromes (ACS) are the leading cause of global death. Optimizing mortality risk prediction and early identification of high-risk patients is essential for developing targeted prevention strategies. Many researchers have built machine learning (ML) models to predict the mortality risk in ACS patients. Our meta-analysis aimed to evaluate the predictive value of various ML models in predicting death in ACS patients at different times. METHODS PubMed, Embase, Web of Science, and Cochrane Library were searched systematically from database establishment to March 12, 2022 for studies developing or validating at least one ML predictive model for death in ACS patients. We used PROBAST to assess the risk of bias in the reported predictive models and a random-effects model to assess the pooled C-index and accuracy of these models. RESULTS Fifty papers were included, involving 216 ML prediction models, 119 of which were externally validated. The combined C-index of the ML models in the validation cohort predicting the in-hospital mortality, 30-day mortality, 3- or 6-month mortality, and 1 year or above mortality in ACS patients were 0.8633 (95% CI 0.8467-0.8802), 0.8296 (95% CI 0.8134-0.8462), 0.8205 (95% CI 0.7881-0.8541), and 0.8197 (95% CI 0.8042-0.8354), respectively, with the corresponding combined accuracy of 0.8569 (95% CI 0.8411-0.8715), 0.8282 (95% CI 0.7922-0.8591), 0.7303 (95% CI 0.7184-0.7418), and 0.7837 (95% CI 0.7455-0.8175), indicating that the ML models were relatively excellent in predicting ACS mortality at different times. Furthermore, common predictors of death in ML models included age, sex, systolic blood pressure, serum creatinine, Killip class, heart rate, diastolic blood pressure, blood glucose, and hemoglobin. CONCLUSIONS The ML models had excellent predictive power for mortality in ACS, and the methodologies may need to be addressed before they can be used in clinical practice.
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Affiliation(s)
- Xiaoxiao Zhang
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Xi Wang
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Luxin Xu
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Jia Liu
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Peng Ren
- School of Life Science and Engineering, Southwest University of Science and Technology, Mianyang, China
| | - Huanlin Wu
- Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
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Li Y, Guan L, Ning C, Zhang P, Zhao Y, Liu Q, Ping P, Fu S. Machine learning-based models to predict one-year mortality among Chinese older patients with coronary artery disease combined with impaired glucose tolerance or diabetes mellitus. Cardiovasc Diabetol 2023; 22:139. [PMID: 37316853 DOI: 10.1186/s12933-023-01854-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 05/10/2023] [Indexed: 06/16/2023] Open
Abstract
PURPOSE An accurate prediction of survival prognosis is beneficial to guide clinical decision-making. This prospective study aimed to develop a model to predict one-year mortality among older patients with coronary artery disease (CAD) combined with impaired glucose tolerance (IGT) or diabetes mellitus (DM) using machine learning techniques. METHODS A total of 451 patients with CAD combined with IGT and DM were finally enrolled, and those patients randomly split 70:30 into training cohort (n = 308) and validation cohort (n = 143). RESULTS The one-year mortality was 26.83%. The least absolute shrinkage and selection operator (LASSO) method and ten-fold cross-validation identified that seven characteristics were significantly associated with one-year mortality with creatine, N-terminal pro-B-type natriuretic peptide (NT-proBNP), and chronic heart failure being risk factors and hemoglobin, high density lipoprotein cholesterol, albumin, and statins being protective factors. The gradient boosting machine model outperformed other models in terms of Brier score (0.114) and area under the curve (0.836). The gradient boosting machine model also showed favorable calibration and clinical usefulness based on calibration curve and clinical decision curve. The Shapley Additive exPlanations (SHAP) found that the top three features associated with one-year mortality were NT-proBNP, albumin, and statins. The web-based application could be available at https://starxueshu-online-application1-year-mortality-main-49cye8.streamlitapp.com/ . CONCLUSIONS This study proposes an accurate model to stratify patients with a high risk of one-year mortality. The gradient boosting machine model demonstrates promising prediction performance. Some interventions to affect NT-proBNP and albumin levels, and statins, are beneficial to improve survival outcome among patients with CAD combined with IGT or DM.
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Affiliation(s)
- Yan Li
- Department of Endocrinology, People's Hospital of Macheng City, Hubei, China
| | - Lixun Guan
- Hematology Department, Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China
| | - Chaoxue Ning
- Central Laboratory, Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China
| | - Pei Zhang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Yali Zhao
- Central Laboratory, Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China.
| | - Qiong Liu
- Medical Care Center, Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China.
| | - Ping Ping
- General Station for Drug and Instrument Supervision and Control, Joint Logistic Support Force of Chinese People's Liberation Army, Beijing, China.
| | - Shihui Fu
- Department of Cardiology, Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya, China.
- Department of Geriatric Cardiology, Chinese People's Liberation Army General Hospital, Beijing, China.
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Sherazi SWA, Zheng H, Lee JY. A Machine Learning-Based Applied Prediction Model for Identification of Acute Coronary Syndrome (ACS) Outcomes and Mortality in Patients during the Hospital Stay. SENSORS (BASEL, SWITZERLAND) 2023; 23:1351. [PMID: 36772390 PMCID: PMC9919937 DOI: 10.3390/s23031351] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 01/17/2023] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
Nowadays, machine learning (ML) is a revolutionary and cutting-edge technology widely used in the medical domain and health informatics in the diagnosis and prognosis of cardiovascular diseases especially. Therefore, we propose a ML-based soft-voting ensemble classifier (SVEC) for the predictive modeling of acute coronary syndrome (ACS) outcomes such as STEMI and NSTEMI, discharge reasons for the patients admitted in the hospitals, and death types for the affected patients during the hospital stay. We used the Korea Acute Myocardial Infarction Registry (KAMIR-NIH) dataset, which has 13,104 patients' data containing 551 features. After data extraction and preprocessing, we used the 125 useful features and applied the SMOTETomek hybrid sampling technique to oversample the data imbalance of minority classes. Our proposed SVEC applied three ML algorithms, such as random forest, extra tree, and the gradient-boosting machine for predictive modeling of our target variables, and compared with the performances of all base classifiers. The experiments showed that the SVEC outperformed other ML-based predictive models in accuracy (99.0733%), precision (99.0742%), recall (99.0734%), F1-score (99.9719%), and the area under the ROC curve (AUC) (99.9702%). Overall, the performance of the SVEC was better than other applied models, but the AUC was slightly lower than the extra tree classifier for the predictive modeling of ACS outcomes. The proposed predictive model outperformed other ML-based models; hence it can be used practically in hospitals for the diagnosis and prediction of heart problems so that timely detection of proper treatments can be chosen, and the occurrence of disease predicted more accurately.
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11
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Hani SHB, Ahmad MM. Machine-learning Algorithms for Ischemic Heart Disease Prediction: A Systematic Review. Curr Cardiol Rev 2023; 19:e090622205797. [PMID: 35692135 PMCID: PMC10201879 DOI: 10.2174/1573403x18666220609123053] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/03/2022] [Accepted: 04/07/2022] [Indexed: 02/08/2023] Open
Abstract
PURPOSE This review aims to summarize and evaluate the most accurate machinelearning algorithm used to predict ischemic heart disease. METHODS This systematic review was performed following PRISMA guidelines. A comprehensive search was carried out using multiple databases such as Science Direct, PubMed\ MEDLINE, CINAHL, and IEEE explore. RESULTS Thirteen articles published between 2017 to 2021 were eligible for inclusion. Three themes were extracted: the commonly used algorithm to predict ischemic heart disease, the accuracy of algorithms to predict ischemic heart disease, and the clinical outcomes to improve the quality of care. All methods have utilized supervised and unsupervised machine-learning. CONCLUSION Applying machine-learning is expected to assist clinicians in interpreting patients' data and implementing optimal algorithms for their datasets. Furthermore, machine-learning can build evidence-based that supports health care providers to manage individual situations who need invasive procedures such as catheterizations.
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Affiliation(s)
- Salam H. Bani Hani
- Faculty of Nursing, The University of Jordan, Amman, Jordan
- Faculty of Nursing, Al Al-Bayt University, Mafraq, Jordan
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12
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Kasim S, Malek S, Song C, Wan Ahmad WA, Fong A, Ibrahim KS, Safiruz MS, Aziz F, Hiew JH, Ibrahim N. In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm. PLoS One 2022; 17:e0278944. [PMID: 36508425 PMCID: PMC9744311 DOI: 10.1371/journal.pone.0278944] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 11/25/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Conventional risk score for predicting in-hospital mortality following Acute Coronary Syndrome (ACS) is not catered for Asian patients and requires different types of scoring algorithms for STEMI and NSTEMI patients. OBJECTIVE To derive a single algorithm using deep learning and machine learning for the prediction and identification of factors associated with in-hospital mortality in Asian patients with ACS and to compare performance to a conventional risk score. METHODS The Malaysian National Cardiovascular Disease Database (NCVD) registry, is a multi-ethnic, heterogeneous database spanning from 2006-2017. It was used for in-hospital mortality model development with 54 variables considered for patients with STEMI and Non-STEMI (NSTEMI). Mortality prediction was analyzed using feature selection methods with machine learning algorithms. Deep learning algorithm using features selected from machine learning was compared to Thrombolysis in Myocardial Infarction (TIMI) score. RESULTS A total of 68528 patients were included in the analysis. Deep learning models constructed using all features and selected features from machine learning resulted in higher performance than machine learning and TIMI risk score (p < 0.0001 for all). The best model in this study is the combination of features selected from the SVM algorithm with a deep learning classifier. The DL (SVM selected var) algorithm demonstrated the highest predictive performance with the least number of predictors (14 predictors) for in-hospital prediction of STEMI patients (AUC = 0.96, 95% CI: 0.95-0.96). In NSTEMI in-hospital prediction, DL (RF selected var) (AUC = 0.96, 95% CI: 0.95-0.96, reported slightly higher AUC compared to DL (SVM selected var) (AUC = 0.95, 95% CI: 0.94-0.95). There was no significant difference between DL (SVM selected var) algorithm and DL (RF selected var) algorithm (p = 0.5). When compared to the DL (SVM selected var) model, the TIMI score underestimates patients' risk of mortality. TIMI risk score correctly identified 13.08% of the high-risk patient's non-survival vs 24.7% for the DL model and 4.65% vs 19.7% of the high-risk patient's non-survival for NSTEMI. Age, heart rate, Killip class, cardiac catheterization, oral hypoglycemia use and antiarrhythmic agent were found to be common predictors of in-hospital mortality across all ML feature selection models in this study. The final algorithm was converted into an online tool with a database for continuous data archiving for prospective validation. CONCLUSIONS ACS patients were better classified using a combination of machine learning and deep learning in a multi-ethnic Asian population when compared to TIMI scoring. Machine learning enables the identification of distinct factors in individual Asian populations to improve mortality prediction. Continuous testing and validation will allow for better risk stratification in the future, potentially altering management and outcomes.
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Affiliation(s)
- Sazzli Kasim
- Cardiology Department, Faculty of Medicine, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
- Cardiac Vascular and Lung Research Institute, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
- National Heart Association of Malaysia, Heart House, Kuala Lumpur, Malaysia
- Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh Campus, Sungai Buloh, Malaysia
| | - Sorayya Malek
- Bioinformatics Division, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
- * E-mail:
| | - Cheen Song
- Bioinformatics Division, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Wan Azman Wan Ahmad
- National Heart Association of Malaysia, Heart House, Kuala Lumpur, Malaysia
- Division of Cardiology, University Malaya Medical Centre, Kuala Lumpur, Malaysia
| | - Alan Fong
- Sarawak Heart Centre, Kota Samarahan, Sarawak, Malaysia
- Clinical Research Centre, Sarawak General Hospital, Institute for Clinical Research, National Institutes of Health, Jalan Hospital, Kuching, Sarawak, Malaysia
- Swinburne University of Technology, Sarawak Campus, Kuching, Malaysia
| | - Khairul Shafiq Ibrahim
- Cardiology Department, Faculty of Medicine, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
- Cardiac Vascular and Lung Research Institute, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia
- National Heart Association of Malaysia, Heart House, Kuala Lumpur, Malaysia
| | - Muhammad Shahreeza Safiruz
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia
| | - Firdaus Aziz
- Bioinformatics Division, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Jia Hui Hiew
- Bioinformatics Division, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Nurulain Ibrahim
- Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh Campus, Sungai Buloh, Malaysia
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Lee W, Schwartz N, Bansal A, Khor S, Hammarlund N, Basu A, Devine B. A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2-Data From Nonwearables. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2022; 25:2053-2061. [PMID: 35989154 DOI: 10.1016/j.jval.2022.07.011] [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: 02/07/2022] [Revised: 06/10/2022] [Accepted: 07/10/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVES Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR. METHODS We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included. Studies related to wearable devices were excluded. We abstracted information on the ML applications, data types, and ML methods and analyzed it using descriptive statistics. RESULTS We retrieved 805 articles, of which 161 (20%) were randomly chosen. Ninety-two of the random sample met the eligibility criteria. We found that ML was primarily used for predicting future events (86%) rather than current events (14%). The most common response variables were clinical events or disease incidence (42%) and treatment outcomes (22%). ML was less used to predict economic outcomes such as health resource utilization (16%) or costs (3%). Although electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (eg, random forests and boosting) were the most commonly used ML methods (31%). CONCLUSIONS The use of ML techniques in HEOR is growing rapidly, but there remain opportunities to apply them to predict economic outcomes, especially using claims databases, which could inform the development of cost-effectiveness models.
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Affiliation(s)
- Woojung Lee
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA.
| | - Naomi Schwartz
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Aasthaa Bansal
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Sara Khor
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Noah Hammarlund
- Department of Health Services Research, Management & Policy, University of Florida, Gainesville, FL, USA
| | - Anirban Basu
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
| | - Beth Devine
- The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, WA, USA
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Acute coronary syndrome risk prediction based on gradient boosted tree feature selection and recursive feature elimination: A dataset-specific modeling study. PLoS One 2022; 17:e0278217. [PMID: 36445881 PMCID: PMC9707772 DOI: 10.1371/journal.pone.0278217] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 11/11/2022] [Indexed: 12/02/2022] Open
Abstract
Acute coronary syndrome (ACS) is a serious cardiovascular disease that can lead to cardiac arrest if not diagnosed promptly. However, in the actual diagnosis and treatment of ACS, there will be a large number of redundant related features that interfere with the judgment of professionals. Further, existing methods have difficulty identifying high-quality ACS features from these data, and the interpretability work is insufficient. In response to this problem, this paper uses a hybrid feature selection method based on gradient boosting trees and recursive feature elimination with cross-validation (RFECV) to reduce ACS feature redundancy and uses interpretable feature learning for feature selection to retain the most discriminative features. While reducing the feature set search space, this method can balance model simplicity and learning performance to select the best feature subset. We leverage the interpretability of gradient boosting trees to aid in understanding key features of ACS, linking the eigenvalue meaning of instances to model risk predictions to provide interpretability for the classifier. The data set used in this paper is patient records after percutaneous coronary intervention (PCI) in a tertiary hospital in Fujian Province, China from 2016 to 2021. In this paper, we experimentally explored the impact of our method on ACS risk prediction. We extracted 25 key variables from 430 complex ACS medical features, with a feature reduction rate of 94.19%, and identified 5 key ACS factors. Compared with different baseline methods (Logistic Regression, Random Forest, Gradient Boosting, Extreme Gradient Boosting, Multilayer Perceptron, and 1D Convolutional Networks), the results show that our method achieves the highest Accuracy of 98.8%.
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15
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Patel S, Thompson W, Sivaswamy A, Khan A, Ferreira-Legere L, Lee DS, Abdel-Qadir H, Jackevicius C, Goodman S, Farkouh ME, Tu K, Kapral MK, Wijeysundera HC, Tam D, Austin PC, Fang J, Ko DT, Udell JA. Development and validation of a model to categorize cardiovascular cause of death using health administrative data. AMERICAN HEART JOURNAL PLUS : CARDIOLOGY RESEARCH AND PRACTICE 2022; 22:100207. [PMID: 38558908 PMCID: PMC10978408 DOI: 10.1016/j.ahjo.2022.100207] [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: 08/22/2022] [Revised: 09/12/2022] [Accepted: 09/13/2022] [Indexed: 04/04/2024]
Abstract
Study objective Develop and evaluate a model that uses health administrative data to categorize cardiovascular (CV) cause of death (COD). Design Population-based cohort. Setting Ontario, Canada. Participants Decedents ≥ 40 years with known COD between 2008 and 2015 in the CANHEART cohort, split into derivation (2008 to 2012; n = 363,778) and validation (2013 to 2015; n = 239,672) cohorts. Main outcome measures Model performance. COD was categorized as CV or non-CV with ICD-10 codes as the gold standard. We developed a logistic regression model that uses routinely collected healthcare administrative to categorize CV versus non-CV COD. We assessed model discrimination and calibration in the validation cohort. Results The strongest predictors for CV COD were history of stroke, history of myocardial infarction, history of heart failure, and CV hospitalization one month before death. In the validation cohort, the c-statistic was 0.80, the sensitivity 0.75 (95 % CI 0.74 to 0.75) and the specificity 0.71 (95 % CI 0.70 to 0.71). In the primary prevention validation sub-cohort, the c-statistic was 0.81, the sensitivity 0.71 (95 % CI 0.70 to 0.71) and the specificity 0.75 (95 % CI 0.75 to 0.75) while in the secondary prevention sub-cohort the c-statistic was 0.74, the sensitivity 0.81 (95 % CI 0.81 to 0.82) and the specificity 0.54 (95 % CI 0.53 to 0.54). Conclusion Modelling approaches using health administrative data show potential in categorizing CV COD, though further work is necessary before this approach is employed in clinical studies.
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Affiliation(s)
- Sagar Patel
- Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Wade Thompson
- Women's College Research Institute, Toronto, Canada
- ICES, Toronto, Canada
- Research Unit of General Practice, University of Southern Denmark, Odense, Denmark
- Department of Anesthesiology, Pharmacology, and Therapeutics, University of British Columbia, Canada
| | | | | | | | - Douglas S. Lee
- ICES, Toronto, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada
- Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Peter Munk Cardiac Centre, University Health Network, Toronto, Canada
| | - Husam Abdel-Qadir
- ICES, Toronto, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada
- Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Peter Munk Cardiac Centre, University Health Network, Toronto, Canada
- Cardiovascular Division, Department of Medicine, Women's College Hospital, Toronto, Canada
| | - Cynthia Jackevicius
- ICES, Toronto, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada
- Western University of Health Sciences, Pomona, CA, United States of America
| | - Shaun Goodman
- Western University of Health Sciences, Pomona, CA, United States of America
- Division of Cardiology, St. Michael's Hospital, Toronto, Canada
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Canada
| | - Michael E. Farkouh
- Peter Munk Cardiac Centre, University Health Network, Toronto, Canada
- Heart and Stroke/Richard Lewar Centre of Excellence, University of Toronto, Toronto, Canada
| | - Karen Tu
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada
- North York General Hospital, Department of Family and Community Medicine, University of Toronto, Toronto, Canada
- Toronto Western Hospital Family Health Team, University Health Network, Toronto, Canada
| | - Moira K. Kapral
- ICES, Toronto, Canada
- Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Harindra C. Wijeysundera
- ICES, Toronto, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada
- Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Schulich Heart Centre, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Derrick Tam
- Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Peter C. Austin
- ICES, Toronto, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada
| | | | - Dennis T. Ko
- ICES, Toronto, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada
- Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Schulich Heart Centre, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Jacob A. Udell
- ICES, Toronto, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Canada
- Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Peter Munk Cardiac Centre, University Health Network, Toronto, Canada
- Cardiovascular Division, Department of Medicine, Women's College Hospital, Toronto, Canada
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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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Bat-Erdene BI, Zheng H, Son SH, Lee JY. Deep learning-based prediction of heart failure rehospitalization during 6, 12, 24-month follow-ups in patients with acute myocardial infarction. Health Informatics J 2022; 28:14604582221101529. [PMID: 35587458 DOI: 10.1177/14604582221101529] [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: 11/16/2022]
Abstract
Heart failure is a clinical syndrome that occurs when the heart is too weak or stiff and cannot pump enough blood that our body needs. It is one of the most expensive diseases due to frequent hospitalizations and emergency room visits. Reducing unnecessary rehospitalizations is also an important and challenging task that has the potential of saving healthcare costs, enabling discharge planning, and identifying patients at high risk. Therefore, this paper proposes a deep learning-based prediction model of heart failure rehospitalization during 6, 12, 24-month follow-ups after hospital discharge in patients with acute myocardial infarction (AMI). We used the Korea Acute Myocardial Infarction-National Institutes of Health (KAMIR-NIH) registry which included 13,104 patient records and 551 features. The proposed deep learning-based rehospitalization prediction model outperformed traditional machine learning algorithms such as logistic regression, support vector machine, AdaBoost, gradient boosting machine, and random forest. The performance of the proposed model was accuracy, the area under the curve, precision, recall, specificity, and F1 score of 99.37%, 99.90%, 96.86%, 98.61%, 99.49%, and 97.73%, respectively. This study showed the potential of a deep learning-based model for cardiology, which can be used for decision-making and medical diagnosis tool of heart failure rehospitalization in patients with AMI.
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Affiliation(s)
- Bat-Ireedui Bat-Erdene
- Department of Computer Science, 34933Chungbuk National University, Cheongju, South Korea
| | - Huilin Zheng
- Department of Computer Science, 34933Chungbuk National University, Cheongju, South Korea
| | - Sang Hyeok Son
- Department of Computer Science, 34933Chungbuk National University, Cheongju, South Korea
| | - Jong Yun Lee
- Department of Computer Science, 34933Chungbuk National University, Cheongju, South Korea
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18
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Lee W, Lee J, Woo SI, Choi SH, Bae JW, Jung S, Jeong MH, Lee WK. Machine learning enhances the performance of short and long-term mortality prediction model in non-ST-segment elevation myocardial infarction. Sci Rep 2021; 11:12886. [PMID: 34145358 PMCID: PMC8213755 DOI: 10.1038/s41598-021-92362-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 06/07/2021] [Indexed: 11/09/2022] Open
Abstract
Machine learning (ML) has been suggested to improve the performance of prediction models. Nevertheless, research on predicting the risk in patients with acute myocardial infarction (AMI) has been limited and showed inconsistency in the performance of ML models versus traditional models (TMs). This study developed ML-based models (logistic regression with regularization, random forest, support vector machine, and extreme gradient boosting) and compared their performance in predicting the short- and long-term mortality of patients with AMI with those of TMs with comparable predictors. The endpoints were the in-hospital mortality of 14,183 participants and the three- and 12-month mortality in patients who survived at discharge. The performance of the ML models in predicting the mortality of patients with an ST-segment elevation myocardial infarction (STEMI) was comparable to the TMs. In contrast, the areas under the curves (AUC) of the ML models for non-STEMI (NSTEMI) in predicting the in-hospital, 3-month, and 12-month mortality were 0.889, 0.849, and 0.860, respectively, which were superior to the TMs, which had corresponding AUCs of 0.873, 0.795, and 0.808. Overall, the performance of the predictive model could be improved, particularly for long-term mortality in NSTEMI, from the ML algorithm rather than using more clinical predictors.
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Affiliation(s)
- Woojoo Lee
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Joongyub Lee
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Seoung-Il Woo
- Department of Cardiology, School of Medicine, Inha University Hospital, Inha University, Incheon, Republic of Korea
| | - Seong Huan Choi
- Department of Cardiology, School of Medicine, Inha University Hospital, Inha University, Incheon, Republic of Korea
| | - Jang-Whan Bae
- Division of Cardiology, Department of Internal Medicine, Chungbuk National University College of Medicine, Cheongju, Republic of Korea
| | - Seungpil Jung
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Myung Ho Jeong
- Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Won Kyung Lee
- Department of Prevention and Management, School of Medicine, Inha University Hospital, Inha University, 27 Inhang-Ro, Jung-Gu, Incheon, Republic of Korea.
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19
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Sherazi SWA, Bae JW, Lee JY. A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients with acute coronary syndrome. PLoS One 2021; 16:e0249338. [PMID: 34115750 PMCID: PMC8195401 DOI: 10.1371/journal.pone.0249338] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 03/16/2021] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE Some researchers have studied about early prediction and diagnosis of major adverse cardiovascular events (MACE), but their accuracies were not high. Therefore, this paper proposes a soft voting ensemble classifier (SVE) using machine learning (ML) algorithms. METHODS We used the Korea Acute Myocardial Infarction Registry dataset and selected 11,189 subjects among 13,104 with the 2-year follow-up. It was subdivided into two groups (ST-segment elevation myocardial infarction (STEMI), non ST-segment elevation myocardial infarction NSTEMI), and then subdivided into training (70%) and test dataset (30%). Third, we selected the ranges of hyper-parameters to find the best prediction model from random forest (RF), extra tree (ET), gradient boosting machine (GBM), and SVE. We generated each ML-based model with the best hyper-parameters, evaluated by 5-fold stratified cross-validation, and then verified by test dataset. Lastly, we compared the performance in the area under the ROC curve (AUC), accuracy, precision, recall, and F-score. RESULTS The accuracies for RF, ET, GBM, and SVE were (88.85%, 88.94%, 87.84%, 90.93%) for complete dataset, (84.81%, 85.00%, 83.70%, 89.07%) STEMI, (88.81%, 88.05%, 91.23%, 91.38%) NSTEMI. The AUC values in RF were (98.96%, 98.15%, 98.81%), ET (99.54%, 99.02%, 99.00%), GBM (98.92%, 99.33%, 99.41%), and SVE (99.61%, 99.49%, 99.42%) for complete dataset, STEMI, and NSTEMI, respectively. Consequently, the accuracy and AUC in SVE outperformed other ML models. CONCLUSIONS The performance of our SVE was significantly higher than other machine learning models (RF, ET, GBM) and its major prognostic factors were different. This paper will lead to the development of early risk prediction and diagnosis tool of MACE in ACS patients.
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Affiliation(s)
| | - Jang-Whan Bae
- Department of Internal Medicine, College of Medicine, Chungbuk National University, Cheongju, Chungbuk, South Korea
| | - Jong Yun Lee
- Department of Computer Science, Chungbuk National University, Cheongju, Chungbuk, South Korea
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20
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Mandair D, Tiwari P, Simon S, Colborn KL, Rosenberg MA. Prediction of incident myocardial infarction using machine learning applied to harmonized electronic health record data. BMC Med Inform Decis Mak 2020; 20:252. [PMID: 33008368 PMCID: PMC7532582 DOI: 10.1186/s12911-020-01268-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 09/17/2020] [Indexed: 12/23/2022] Open
Abstract
Background With cardiovascular disease increasing, substantial research has focused on the development of prediction tools. We compare deep learning and machine learning models to a baseline logistic regression using only ‘known’ risk factors in predicting incident myocardial infarction (MI) from harmonized EHR data. Methods Large-scale case-control study with outcome of 6-month incident MI, conducted using the top 800, from an initial 52 k procedures, diagnoses, and medications within the UCHealth system, harmonized to the Observational Medical Outcomes Partnership common data model, performed on 2.27 million patients. We compared several over- and under- sampling techniques to address the imbalance in the dataset. We compared regularized logistics regression, random forest, boosted gradient machines, and shallow and deep neural networks. A baseline model for comparison was a logistic regression using a limited set of ‘known’ risk factors for MI. Hyper-parameters were identified using 10-fold cross-validation. Results Twenty thousand Five hundred and ninety-one patients were diagnosed with MI compared with 2.25 million who did not. A deep neural network with random undersampling provided superior classification compared with other methods. However, the benefit of the deep neural network was only moderate, showing an F1 Score of 0.092 and AUC of 0.835, compared to a logistic regression model using only ‘known’ risk factors. Calibration for all models was poor despite adequate discrimination, due to overfitting from low frequency of the event of interest. Conclusions Our study suggests that DNN may not offer substantial benefit when trained on harmonized data, compared to traditional methods using established risk factors for MI.
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Affiliation(s)
- Divneet Mandair
- Division of Internal Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Premanand Tiwari
- Colorado Center for Personalized Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Steven Simon
- Division of Cardiology and Cardiac Electrophysiology, University of Colorado School of Medicine, 12631 E. 17th Avenue, Mail Stop B130, Aurora, CO, 80045, USA
| | - Kathryn L Colborn
- Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA
| | - Michael A Rosenberg
- Division of Internal Medicine, University of Colorado School of Medicine, Aurora, CO, USA. .,Division of Cardiology and Cardiac Electrophysiology, University of Colorado School of Medicine, 12631 E. 17th Avenue, Mail Stop B130, Aurora, CO, 80045, USA.
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