Key result
Machine learning models, specifically XGBoost and random forest, accurately predicted physical function upon discharge among hospitalized older adults with an auROC of 98% using electronic health records and comprehensive geriatric assessments.
Why the study?
Predicting physical function upon discharge among hospitalized older adults is important.
Observational (n=1,755)
No
Effect estimate: auROC 98%
Absolute Event Rate: 94% vs 92%
Machine learning models utilizing electronic health records and comprehensive geriatric assessments can accurately predict physical function upon discharge in hospitalized older adults.
ML models may aid discharge planning in older inpatients; hypothesis-generating pending prospective validation.
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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Chu et al. (2023) conducted an observational in Hospitalized older adults (n=1,755). Machine learning models (XGBoost, random forest) using EHR and CGA data vs. Logistic regression was evaluated on Accuracy and auROC for predicting physical function upon discharge (ADL > 50) (auROC 98%). Machine learning models, specifically XGBoost and random forest, accurately predicted physical function upon discharge among hospitalized older adults with an auROC of 98% using electronic health records and comprehensive geriatric assessments.
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