Identification of medication-related fall risk in adults and older adults admitted to hospital: A machine learning approach

Abstract

The study aimed to develop and validate, through machine learning, a fall risk prediction model related to prescribed medications specific to adults and older adults admitted to hospital. A case-control study was carried out in a tertiary hospital, involving 9,037 adults and older adults admitted to hospital in 2016. The variables were analyzed using the algorithms: logistic regression, naive bayes, random forest and gradient boosting. The best model presented an area under the curve = 0.628 in the older adult subgroup, compared to an area under the curve (AUC) = 0.776 in the adult subgroup. A specific model was developed for this sample. The gradient boosting model presented the best performance in the sample of older adults (AUC = 0.71). Models developed to predict the risk of falls based on medications specifically aimed at older adults presented better performance in relation to models developed in the total population studied.

Keywords: Accidental falls; Aged; Drug utilization; Hospitals; Supervised learning.