Development of a clinical support system for identifying social frailty. (December 2019)

• Record Type: Journal Article
• Authors: Kuo, Kuang-Ming; Talley, Paul C.; Kuzuya, Masafumi; Huang, Chi-Hsien
• Is Part Of: International journal of medical informatics. Volume 132(2019)
• Publication Date: 2019-12
• Subjects: Health literacy -- Machine learning -- Social frailty; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2019.103979
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: What are some of the highlights to be realized from this study? A social frailty prediction system, one based on a non-electronic database, performed well. Health literacy is the most important predictor of social frailty. The web-based prediction system could help physicians to screen for social frailty. Social frailty is of the same importance as physical frailty. Abstract: Objective: Recognizing frailty, also known as clinical geriatric syndrome in the elderly that is characterized by high vulnerability and low resilience, and its extensive influence in clinical practice is challenging. This study aims to develop a social frailty prediction system based on machine learning approaches in order to identify the social frailty status of the elders in order to advance appropriate social services provision. Materials and methods: This cross-sectional study enrolled and collected information from 595 community-dwelling seniors aged 65+. Fourteen predictors established from questionnaires and electronic medical records were used to predict the social frailty of participants. Bagged classification and regression trees, model average neural network, random forest, C5.0, eXtreme gradient boosting, and stochastic gradient boosting were used to build the predictive model in use. Performance was compared using accuracy, kappa, area under receiver operating characteristic curve, sensitivity, and specificity. The frailty predictive system was web-based and built upon representational state transfer application program interfaces. Results: C5.0 achieved the best overall performance than remaining learners, and was adopted as the base learner for the social frailty prediction system. In terms of the area under receiver operating characteristic curve (AUC), health literacy (AUC = 0.68) was found to be the most important variable for predicting one's social frailty, followed by comorbidity (AUC = 0.67), religious participation (AUC = 0.67), physical activity (AUC = 0.66), and geriatric depression score (AUC = 0.62). Conclusions: Results suggest that a combination of such data that is both available and unavailable from electronic medical records is predictive of the social frailty of an elderly population.