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Lee WT, Fang YW, Chang WS, Hsiao KY, Shia BC, Chen M, Tsai MH. Data-driven, two-stage machine learning algorithm-based prediction scheme for assessing 1-year and 3-year mortality risk in chronic hemodialysis patients. Sci Rep 2023; 13:21453. [PMID: 38052875 PMCID: PMC10698192 DOI: 10.1038/s41598-023-48905-9] [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: 09/30/2023] [Accepted: 12/01/2023] [Indexed: 12/07/2023] Open
Abstract
Life expectancy is likely to be substantially reduced in patients undergoing chronic hemodialysis (CHD). However, machine learning (ML) may predict the risk factors of mortality in patients with CHD by analyzing the serum laboratory data from regular dialysis routine. This study aimed to establish the mortality prediction model of CHD patients by adopting two-stage ML algorithm-based prediction scheme, combined with importance of risk factors identified by different ML methods. This is a retrospective, observational cohort study. We included 800 patients undergoing CHD between December 2006 and December 2012 in Shin-Kong Wu Ho-Su Memorial Hospital. This study analyzed laboratory data including 44 indicators. We used five ML methods, namely, logistic regression (LGR), decision tree (DT), random forest (RF), gradient boosting (GB), and eXtreme gradient boosting (XGB), to develop a two-stage ML algorithm-based prediction scheme and evaluate the important factors that predict CHD mortality. LGR served as a bench method. Regarding the validation and testing datasets from 1- and 3-year mortality prediction model, the RF had better accuracy and area-under-curve results among the five different ML methods. The stepwise RF model, which incorporates the most important factors of CHD mortality risk based on the average rank from DT, RF, GB, and XGB, exhibited superior predictive performance compared to LGR in predicting mortality among CHD patients over both 1-year and 3-year periods. We had developed a two-stage ML algorithm-based prediction scheme by implementing the stepwise RF that demonstrated satisfactory performance in predicting mortality in patients with CHD over 1- and 3-year periods. The findings of this study can offer valuable information to nephrologists, enhancing patient-centered decision-making and increasing awareness about risky laboratory data, particularly for patients with a high short-term mortality risk.
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Affiliation(s)
- Wen-Teng Lee
- Division of Nephrology, Department of Internal Medicine, Shin-Kong Wu Ho-Su Memorial Hospital, No. 95, Wen-Chang Rd, Shih-Lin Dist., Taipei, 11101, Taiwan
| | - Yu-Wei Fang
- Division of Nephrology, Department of Internal Medicine, Shin-Kong Wu Ho-Su Memorial Hospital, No. 95, Wen-Chang Rd, Shih-Lin Dist., Taipei, 11101, Taiwan
- Department of Medicine, Fu Jen Catholic University, No. 510, Zhongzhen Rd., Xinzhuang Dist., New Taipei City, 24205, Taiwan
| | - Wei-Shan Chang
- Artificial Intelligence Development Center, Fu Jen Catholic University, No. 510, Zhongzhen Rd., Xinzhuang Dist., New Taipei City, 24205, Taiwan
- Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, No. 510, Zhongzhen Rd., Xinzhuang Dist, New Taipei City, 24205, Taiwan
| | - Kai-Yuan Hsiao
- Artificial Intelligence Development Center, Fu Jen Catholic University, No. 510, Zhongzhen Rd., Xinzhuang Dist., New Taipei City, 24205, Taiwan
- Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, No. 510, Zhongzhen Rd., Xinzhuang Dist, New Taipei City, 24205, Taiwan
| | - Ben-Chang Shia
- Artificial Intelligence Development Center, Fu Jen Catholic University, No. 510, Zhongzhen Rd., Xinzhuang Dist., New Taipei City, 24205, Taiwan
- Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, No. 510, Zhongzhen Rd., Xinzhuang Dist, New Taipei City, 24205, Taiwan
| | - Mingchih Chen
- Artificial Intelligence Development Center, Fu Jen Catholic University, No. 510, Zhongzhen Rd., Xinzhuang Dist., New Taipei City, 24205, Taiwan.
- Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, No. 510, Zhongzhen Rd., Xinzhuang Dist, New Taipei City, 24205, Taiwan.
| | - Ming-Hsien Tsai
- Division of Nephrology, Department of Internal Medicine, Shin-Kong Wu Ho-Su Memorial Hospital, No. 95, Wen-Chang Rd, Shih-Lin Dist., Taipei, 11101, Taiwan.
- Department of Medicine, Fu Jen Catholic University, No. 510, Zhongzhen Rd., Xinzhuang Dist., New Taipei City, 24205, Taiwan.
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Dong J, Wang K, He J, Guo Q, Min H, Tang D, Zhang Z, Zhang C, Zheng F, Li Y, Xu H, Wang G, Luan S, Yin L, Zhang X, Dai Y. Machine learning-based intradialytic hypotension prediction of patients undergoing hemodialysis: A multicenter retrospective study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107698. [PMID: 37429246 DOI: 10.1016/j.cmpb.2023.107698] [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: 06/10/2022] [Revised: 05/22/2023] [Accepted: 06/24/2023] [Indexed: 07/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Intradialytic hypotension (IDH) is closely associated with adverse clinical outcomes in HD-patients. An IDH predictor model is important for IDH risk screening and clinical decision-making. In this study, we used Machine learning (ML) to develop IDH model for risk prediction in HD patients. METHODS 62,227 dialysis sessions were randomly partitioned into training data (70%), test data (20%), and validation data (10%). IDH-A model based on twenty-seven variables was constructed for risk prediction for the next HD treatment. IDH-B model based on ten variables from 64,870 dialysis sessions was developed for risk assessment before each HD treatment. Light Gradient Boosting Machine (LightGBM), Linear Discriminant Analysis, support vector machines, XGBoost, TabNet, and multilayer perceptron were used to develop the predictor model. RESULTS In IDH-A model, we identified the LightGBM method as the best-performing and interpretable model with C- statistics of 0.82 in Fall30Nadir90 definitions, which was higher than those obtained using the other models (P<0.01). In other IDH standards of Nadir90, Nadir100, Fall20, Fall30, and Fall20Nadir90, the LightGBM method had a performance with C- statistics ranged 0.77 to 0.89. As a complementary application, the LightGBM model in IDH-B model achieved C- statistics of 0.68 in Fall30Nadir90 definitions and 0.69 to 0.78 in the other five IDH standards, which were also higher than the other methods, respectively. CONCLUSION Use ML, we identified the LightGBM method as the good-performing and interpretable model. We identified the top variables as the high-risk factors for IDH incident in HD-patient. IDH-A and IDH-B model can usefully complement each other for risk prediction and further facilitate timely intervention through applied into different clinical setting.
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Affiliation(s)
- Jingjing Dong
- Clinical Medical Research Center, the Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China; Institute of Nephrology and Blood Purification, the First Affiliated Hospital of Jinan University, Jinan University, Guangzhou 510630, China
| | - Kang Wang
- Department of Nephrology, the Second Affiliated Hospital of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China
| | - Jingquan He
- Clinical Medical Research Center, the Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China
| | - Qi Guo
- Shenzhen Yuchen Medical Technology Co., Ltd. Co., Ltd, Shenzhen 518020, China
| | - Haodi Min
- Shenzhen Yuchen Medical Technology Co., Ltd. Co., Ltd, Shenzhen 518020, China
| | - Donge Tang
- Clinical Medical Research Center, the Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China
| | - Zeyu Zhang
- Clinical Medical Research Center, the Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China; Institute of Nephrology and Blood Purification, the First Affiliated Hospital of Jinan University, Jinan University, Guangzhou 510630, China
| | - Cantong Zhang
- Clinical Medical Research Center, the Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China
| | - Fengping Zheng
- Clinical Medical Research Center, the Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China
| | - Yixi Li
- Clinical Medical Research Center, the Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China; Institute of Nephrology and Blood Purification, the First Affiliated Hospital of Jinan University, Jinan University, Guangzhou 510630, China
| | - Huixuan Xu
- Clinical Medical Research Center, the Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China
| | - Gang Wang
- Department of Nephrology, University of Chinese Academy of Sciences Shenzhen Hospital (Guangming), Shenzhen 518020, China
| | - Shaodong Luan
- Departments of Nephrology, Shenzhen Longhua District Central Hospital, Shenzhen 518020, China
| | - Lianghong Yin
- Institute of Nephrology and Blood Purification, the First Affiliated Hospital of Jinan University, Jinan University, Guangzhou 510630, China.
| | - Xinzhou Zhang
- Department of Nephrology, the Second Affiliated Hospital of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China.
| | - Yong Dai
- Clinical Medical Research Center, the Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Jinan University, Shenzhen 518020, China.
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Eysenbach G, Chao HJ, Chiang YC, Chen HY. Explainable Machine Learning Techniques To Predict Amiodarone-Induced Thyroid Dysfunction Risk: Multicenter, Retrospective Study With External Validation. J Med Internet Res 2023; 25:e43734. [PMID: 36749620 PMCID: PMC9944157 DOI: 10.2196/43734] [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: 10/25/2022] [Revised: 12/25/2022] [Accepted: 01/16/2023] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Machine learning offers new solutions for predicting life-threatening, unpredictable amiodarone-induced thyroid dysfunction. Traditional regression approaches for adverse-effect prediction without time-series consideration of features have yielded suboptimal predictions. Machine learning algorithms with multiple data sets at different time points may generate better performance in predicting adverse effects. OBJECTIVE We aimed to develop and validate machine learning models for forecasting individualized amiodarone-induced thyroid dysfunction risk and to optimize a machine learning-based risk stratification scheme with a resampling method and readjustment of the clinically derived decision thresholds. METHODS This study developed machine learning models using multicenter, delinked electronic health records. It included patients receiving amiodarone from January 2013 to December 2017. The training set was composed of data from Taipei Medical University Hospital and Wan Fang Hospital, while data from Taipei Medical University Shuang Ho Hospital were used as the external test set. The study collected stationary features at baseline and dynamic features at the first, second, third, sixth, ninth, 12th, 15th, 18th, and 21st months after amiodarone initiation. We used 16 machine learning models, including extreme gradient boosting, adaptive boosting, k-nearest neighbor, and logistic regression models, along with an original resampling method and 3 other resampling methods, including oversampling with the borderline-synthesized minority oversampling technique, undersampling-edited nearest neighbor, and over- and undersampling hybrid methods. The model performance was compared based on accuracy; Precision, recall, F1-score, geometric mean, area under the curve of the receiver operating characteristic curve (AUROC), and the area under the precision-recall curve (AUPRC). Feature importance was determined by the best model. The decision threshold was readjusted to identify the best cutoff value and a Kaplan-Meier survival analysis was performed. RESULTS The training set contained 4075 patients from Taipei Medical University Hospital and Wan Fang Hospital, of whom 583 (14.3%) developed amiodarone-induced thyroid dysfunction, while the external test set included 2422 patients from Taipei Medical University Shuang Ho Hospital, of whom 275 (11.4%) developed amiodarone-induced thyroid dysfunction. The extreme gradient boosting oversampling machine learning model demonstrated the best predictive outcomes among all 16 models. The accuracy; Precision, recall, F1-score, G-mean, AUPRC, and AUROC were 0.923, 0.632, 0.756, 0.688, 0.845, 0.751, and 0.934, respectively. After readjusting the cutoff, the best value was 0.627, and the F1-score reached 0.699. The best threshold was able to classify 286 of 2422 patients (11.8%) as high-risk subjects, among which 275 were true-positive patients in the testing set. A shorter treatment duration; higher levels of thyroid-stimulating hormone and high-density lipoprotein cholesterol; and lower levels of free thyroxin, alkaline phosphatase, and low-density lipoprotein were the most important features. CONCLUSIONS Machine learning models combined with resampling methods can predict amiodarone-induced thyroid dysfunction and serve as a support tool for individualized risk prediction and clinical decision support.
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Affiliation(s)
| | - Horng-Jiun Chao
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
| | - Yi-Chun Chiang
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan.,Department of Pharmacy, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Hsiang-Yin Chen
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, Taiwan.,Department of Pharmacy, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
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Othman M, Elbasha AM, Naga YS, Moussa ND. Early prediction of hemodialysis complications employing ensemble techniques. Biomed Eng Online 2022; 21:74. [PMID: 36221077 PMCID: PMC9552449 DOI: 10.1186/s12938-022-01044-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 09/23/2022] [Indexed: 11/10/2022] Open
Abstract
Background and objectives Hemodialysis complications remain a critical threat among dialysis patients. They result in sudden termination of the session which impacts the efficiency of dialysis. As intra-dialytic complications are the result of the interplay of multiple factors, artificial intelligence can aid in their early prediction. This research aims to compare different machine learning tools for the early prediction of the most frequent hemodialysis complications with high performance, using the fewest predictors for easier practical implementation. Methods Fifty different variables were recorded during 6000 hemodialysis sessions performed in a regional dialysis unit in Egypt. The filter technique was used to extract the most relevant features. Then, five individual classifiers and three ensemble approaches were implemented to predict the occurrence of intra-dialytic complications. Different subsets of 25, 12 and 6 from the 50 collected features were tested. Results Random forest yielded the highest accuracy of 98% with the least training time using 12 features in a balanced dataset, while the gradient boosting allowed obtaining the highest F1-score of 94%, 92%, and 78% in the prediction of hypotension, hypertension, and dyspnea, respectively, in imbalanced datasets. Conclusion Applying different machine learning algorithms to big datasets can improve accuracy, reduce training time and model complexity allowing simple implementation in clinical practice. Our models can help nephrologists predict and possibly prevent dialysis complications. Supplementary Information The online version contains supplementary material available at 10.1186/s12938-022-01044-0.
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Affiliation(s)
- Mai Othman
- Biomedical Engineering Department, Medical Research Institute, Alexandria University, 165, Horreya Avenue, Hadara, Alexandria Governorate, Alexandria, Egypt
| | - Ahmed Mustafa Elbasha
- Internal Medicine Department, Faculty of Medicine, Alexandria University, Champollion Street, El-Khartoum Square, El Azareeta Medical Campus, Alexandria Governorate, Alexandria, Egypt
| | - Yasmine Salah Naga
- Internal Medicine Department, Faculty of Medicine, Alexandria University, Champollion Street, El-Khartoum Square, El Azareeta Medical Campus, Alexandria Governorate, Alexandria, Egypt
| | - Nancy Diaa Moussa
- Biomedical Engineering Department, Medical Research Institute, Alexandria University, 165, Horreya Avenue, Hadara, Alexandria Governorate, Alexandria, Egypt.
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Elbasha AM, Naga YS, Othman M, Moussa ND, Elwakil HS. A step towards the application of an artificial intelligence model in the prediction of intradialytic complications. ALEXANDRIA JOURNAL OF MEDICINE 2022. [DOI: 10.1080/20905068.2021.2024349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Affiliation(s)
- Ahmed Mustafa Elbasha
- Department of Internal Medicine, Nephrology Unit, Faculty of Medicine, Alexandria, Egypt
| | - Yasmine Salah Naga
- Department of Internal Medicine, Nephrology Unit, Faculty of Medicine, Alexandria, Egypt
| | - Mai Othman
- Department of Biomedical Engineering, Medical Research Institute, Alexandria, Egypt
| | - Nancy Diaa Moussa
- Department of Biomedical Engineering, Medical Research Institute, Alexandria, Egypt
| | - Hala Sadik Elwakil
- Department of Internal Medicine, Nephrology Unit, Faculty of Medicine, Alexandria, Egypt
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