Prognosis of fall risk in home care clients: A noninvasive approach using survival analysis

Abstract

Abstract Aim The present study has designed, implemented, and evaluated a machine learning model that can predict fall risk and fall occurrence in community-dwelling elderly based on their home care usage. Subjects and methods A dataset consisting of 2542 weekly home care records for 1499 citizens (59% female, 41% male) with a mean age of 77 years (SD 10 years) was collected from a large municipality in Denmark. The data were recorded between January 1, 2021, and December 31, 2021. The dataset was divided into two cohorts. Subsequently, five machine learning-based survival analysis models were trained and evaluated using cross-validation. Results The CoxBoost model showed the best discriminative performance with a mean 0.64 (95% CI 0.57–0.72) Harrell’s concordance index, indicating better ranking than chance-level by 14% on average. However, the model could not accurately predict when the next fall would take place. Conclusions The proposed method enables professionals to assess individual fall risk by using home care records from an Electronic Health Record (EHR) system. This facilitates the initiation of targeted fall-prevention programs for those at highest risk. Additionally, it is expected that a risk-based approach can lead to a lower number needed to treat (NNT), indicating greater effectiveness of health interventions.