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Chu WM, Tsan YT, Chen PY, Chen CY, Hao ML, Chan WC, Chen HM, Hsu PS, Lin SY, Yang CT. A model for predicting physical function upon discharge of hospitalized older adults in Taiwan-a machine learning approach based on both electronic health records and comprehensive geriatric assessment. Front Med (Lausanne) 2023; 10:1160013. [PMID: 37547611 PMCID: PMC10400801 DOI: 10.3389/fmed.2023.1160013] [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: 02/06/2023] [Accepted: 07/03/2023] [Indexed: 08/08/2023] Open
Abstract
Background Predicting physical function upon discharge among hospitalized older adults is important. This study has aimed to develop a prediction model of physical function upon discharge through use of a machine learning algorithm using electronic health records (EHRs) and comprehensive geriatrics assessments (CGAs) among hospitalized older adults in Taiwan. Methods Data was retrieved from the clinical database of a tertiary medical center in central Taiwan. Older adults admitted to the acute geriatric unit during the period from January 2012 to December 2018 were included for analysis, while those with missing data were excluded. From data of the EHRs and CGAs, a total of 52 clinical features were input for model building. We used 3 different machine learning algorithms, XGBoost, random forest and logistic regression. Results In total, 1,755 older adults were included in final analysis, with a mean age of 80.68 years. For linear models on physical function upon discharge, the accuracy of prediction was 87% for XGBoost, 85% for random forest, and 32% for logistic regression. For classification models on physical function upon discharge, the accuracy for random forest, logistic regression and XGBoost were 94, 92 and 92%, respectively. The auROC reached 98% for XGBoost and random forest, while logistic regression had an auROC of 97%. The top 3 features of importance were activity of daily living (ADL) at baseline, ADL during admission, and mini nutritional status (MNA) during admission. Conclusion The results showed that physical function upon discharge among hospitalized older adults can be predicted accurately during admission through use of a machine learning model with data taken from EHRs and CGAs.
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Affiliation(s)
- Wei-Min Chu
- Department of Family Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- Education and Innovation Center for Geriatrics and Gerontology, National Center for Geriatrics and Gerontology, Ōbu, Japan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan
- Geriatrics and Gerontology Research Center, College of Medicine, National Chung Hsing University, Taichung, Taiwan
| | - Yu-Tse Tsan
- Geriatrics and Gerontology Research Center, College of Medicine, National Chung Hsing University, Taichung, Taiwan
- Department of Occupational Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Pei-Yu Chen
- Department of Family Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Chia-Yu Chen
- Department of Family Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Man-Ling Hao
- Department of Computer Science, Tunghai University, Taichung, Taiwan
| | - Wei-Chan Chan
- Department of Occupational Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Hong-Ming Chen
- Department of Applied Mathematics, Tunghai University, Taichung, Taiwan
| | - Pi-Shan Hsu
- Department of Family Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Shih-Yi Lin
- Geriatrics and Gerontology Research Center, College of Medicine, National Chung Hsing University, Taichung, Taiwan
- Center for Geriatrics and Gerontology, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Chao-Tung Yang
- Department of Computer Science, Tunghai University, Taichung, Taiwan
- Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung, Taiwan
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Fan S, Ye J, Xu Q, Peng R, Hu B, Pei Z, Yang Z, Xu F. Digital health technology combining wearable gait sensors and machine learning improve the accuracy in prediction of frailty. Front Public Health 2023; 11:1169083. [PMID: 37546315 PMCID: PMC10402732 DOI: 10.3389/fpubh.2023.1169083] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 06/30/2023] [Indexed: 08/08/2023] Open
Abstract
Background Frailty is a dynamic and complex geriatric condition characterized by multi-domain declines in physiological, gait and cognitive function. This study examined whether digital health technology can facilitate frailty identification and improve the efficiency of diagnosis by optimizing analytical and machine learning approaches using select factors from comprehensive geriatric assessment and gait characteristics. Methods As part of an ongoing study on observational study of Aging, we prospectively recruited 214 individuals living independently in the community of Southern China. Clinical information and fragility were assessed using comprehensive geriatric assessment (CGA). Digital tool box consisted of wearable sensor-enabled 6-min walk test (6MWT) and five machine learning algorithms allowing feature selections and frailty classifications. Results It was found that a model combining CGA and gait parameters was successful in predicting frailty. The combination of these features in a machine learning model performed better than using either CGA or gait parameters alone, with an area under the curve of 0.93. The performance of the machine learning models improved by 4.3-11.4% after further feature selection using a smaller subset of 16 variables. SHapley Additive exPlanation (SHAP) dependence plot analysis revealed that the most important features for predicting frailty were large-step walking speed, average step size, age, total step walking distance, and Mini Mental State Examination score. Conclusion This study provides evidence that digital health technology can be used for predicting frailty and identifying the key gait parameters in targeted health assessments.
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Affiliation(s)
- Shaoyi Fan
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jieshun Ye
- School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China
| | - Qing Xu
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Runxin Peng
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Bin Hu
- Division of Translational Neuroscience, Department of Clinical Neurosciences, Hotchkiss Brain Institute, Alberta Children’s Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Zhong Pei
- Department of Neurology, First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Zhimin Yang
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Fuping Xu
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
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Hasegawa S, Mizokami F, Kameya Y, Hayakawa Y, Watanabe T, Matsui Y. Machine learning versus binomial logistic regression analysis for fall risk based on SPPB scores in older adult outpatients. Digit Health 2023; 9:20552076231219438. [PMID: 38107982 PMCID: PMC10722919 DOI: 10.1177/20552076231219438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/21/2023] [Indexed: 12/19/2023] Open
Abstract
Objective To compare the performance of the diagnostic model for fall risk based on the short physical performance battery (SPPB) developed using commercial machine learning software (MLS) and binomial logistic regression analysis (BLRA). Methods We enrolled 797 out of 850 outpatients who visited the clinic between March 2016 and November 2021. Patients were categorized into the development (n = 642) and validation (n = 155) datasets. Age, sex, number of comorbidities, number of medications, body mass index (BMI), calf circumference (left-right average), handgrip strength (left-right average), total SPPB score, and history of falls were determined. We defined fall risk by an SPPB score of ≤6 in men and ≤9 in women. The main metrics used for evaluating the machine learning model and BLRA were the area under the curve (AUC), accuracy, precision, recall (sensitivity), specificity, and F-measure. The commercial MLS automatically calculates the parameter range of the highest contribution. Results The participants included 797 outpatients (mean age, 76.3 years; interquartile range, 73.0-81.0; 288 men). The metrics of the current diagnostic model in the commercial MLS were as follows: AUC = 0.78, accuracy = 0.74, precision = 0.46, recall (sensitivity) = 0.81, specificity = 0.71, F-measure = 0.59. The metrics of the current diagnostic model in the BLRA were as follows: AUC = 0.77, accuracy = 0.75, precision = 0.47, recall (sensitivity) = 0.67, specificity = 0.77, F-measure = 0.55. The risk factors for falls in older adult outpatients were handgrip strength, female sex, experience of falls, BMI, and calf circumference in the commercial MLS. Conclusions The diagnostic model for fall risk based on SPPB scores constructed using commercial MLS is noninferior to BLRA.
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Affiliation(s)
- Sho Hasegawa
- Department of Pharmacotherapeutics and Informatics, Fujita Health University, Toyoake, Japan
- Department of Education and Innovation, Training for Pharmacy, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Fumihiro Mizokami
- Department of Education and Innovation, Training for Pharmacy, National Center for Geriatrics and Gerontology, Obu, Japan
- Department of Pharmacy, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Yoshitaka Kameya
- Department of Information Engineering, Meijo University, Nagoya, Japan
| | - Yuji Hayakawa
- Department of Education and Innovation, Training for Pharmacy, National Center for Geriatrics and Gerontology, Obu, Japan
- Department of Pharmacy, National Hospital Organization, Nagoya Medical Center, Nagoya, Japan
| | - Tsuyoshi Watanabe
- Department of Orthopaedic Surgery, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Yasumoto Matsui
- Center for Frailty and Locomotive Syndrome, National Center for Geriatrics and Gerontology, Obu, Japan
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Zhao F, Zhang H, Cheng D, Wang W, Li Y, Wang Y, Lu D, Dong C, Ren D, Yang L. Predicting the risk of nodular thyroid disease in coal miners based on different machine learning models. Front Med (Lausanne) 2022; 9:1037944. [PMID: 36507527 PMCID: PMC9732087 DOI: 10.3389/fmed.2022.1037944] [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: 09/06/2022] [Accepted: 11/11/2022] [Indexed: 11/27/2022] Open
Abstract
Background Nodular thyroid disease is by far the most common thyroid disease and is closely associated with the development of thyroid cancer. Coal miners with chronic coal dust exposure are at higher risk of developing nodular thyroid disease. There are few studies that use machine learning models to predict the occurrence of nodular thyroid disease in coal miners. The aim of this study was to predict the high risk of nodular thyroid disease in coal miners based on five different Machine learning (ML) models. Methods This is a retrospective clinical study in which 1,708 coal miners who were examined at the Huaihe Energy Occupational Disease Control Hospital in Anhui Province in April 2021 were selected and their clinical physical examination data, including general information, laboratory tests and imaging findings, were collected. A synthetic minority oversampling technique (SMOTE) was used for sample balancing, and the data set was randomly split into a training and Test dataset in a ratio of 8:2. Lasso regression and correlation heat map were used to screen the predictors of the models, and five ML models, including Extreme Gradient Augmentation (XGBoost), Logistic Classification (LR), Gaussian Parsimonious Bayesian Classification (GNB), Neural Network Classification (MLP), and Complementary Parsimonious Bayesian Classification (CNB) for their predictive efficacy, and the model with the highest AUC was selected as the optimal model for predicting the occurrence of nodular thyroid disease in coal miners. Result Lasso regression analysis showed Age, H-DLC, HCT, MCH, PLT, and GGT as predictor variables for the ML models; in addition, heat maps showed no significant correlation between the six variables. In the prediction of nodular thyroid disease, the AUC results of the five ML models, XGBoost (0.892), LR (0.577), GNB (0.603), MLP (0.601), and CNB (0.543), with the XGBoost model having the largest AUC, the model can be applied in clinical practice. Conclusion In this research, all five ML models were found to predict the risk of nodular thyroid disease in coal miners, with the XGBoost model having the best overall predictive performance. The model can assist clinicians in quickly and accurately predicting the occurrence of nodular thyroid disease in coal miners, and in adopting individualized clinical prevention and treatment strategies.
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Affiliation(s)
- Feng Zhao
- The First Hospital of Anhui University of Science & Technology (Huainan First People’s Hospital), Huainan, China
| | - Hongzhen Zhang
- Anhui University of Science and Technology College of Medicine, Huainan, China
| | - Danqing Cheng
- Graduate School of Bengbu Medical College, Bengbu, China
| | - Wenping Wang
- Graduate School of Bengbu Medical College, Bengbu, China
| | - Yongtian Li
- Anhui University of Science and Technology College of Medicine, Huainan, China
| | - Yisong Wang
- Anhui University of Science and Technology College of Medicine, Huainan, China
| | - Dekun Lu
- The First Hospital of Anhui University of Science & Technology (Huainan First People’s Hospital), Huainan, China
| | - Chunhui Dong
- Anhui University of Science and Technology College of Medicine, Huainan, China
| | - Dingfei Ren
- Occupational Control Hospital of Huai He Energy Group, Huainan, Anhui, China
| | - Lixin Yang
- The First Hospital of Anhui University of Science & Technology (Huainan First People’s Hospital), Huainan, China,*Correspondence: Lixin Yang,
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