Fall Risk Assessment and Ranking using Survival Analysis

Abstract Falling is the second leading cause of unintentional injury-related deaths, but fall injuries in older adults are often underreported, reported too late or reported with a lack of detail due to the stigma that surrounds falling. This makes it hard to locate and target the right citizens with fall prevention programs. In this paper, we propose a novel approach to assess fall risk over time and thus identify who would benefit from such a program. Using survival analysis, we train machine learning models to rank citizens by their predicted risk of getting a personal alarm within six months based on home care observations. Data were gathered from July 2021 to December 2021 for 57 types of home care from a large municipality in Denmark. After preprocessing, a total of 5713 observations for 346 citizens with mean age 81 (std. 5 years) are available for model training. Results show that a plain Cox proportional hazards model obtain a mean concordance index of 0.76 (95\% CI: 0.7-0.82), Random Survival Forest obtain 0.84 (95\% CI: 0.76-0.92) and XGBoost obtain 0.94 (95\% CI: 0.88-1) over 25 randomized folds using cross-validation. Censoring rate is 94.5\% on average. Most predictive features are help with personal hygiene, administration of medicine, excretions, cleaning and dispensing of medicine according to a SHAP summary plot. We also identify correlations between care usage in minutes per week and risk using SHAP dependency plots, but the correlation is not always positive. With our method, professionals at municipalities in Denmark or elsewhere, where home care services are recorded, will have a noninvasive decision-support tool that can predict individual fall risk over time.