Predictive modelling using machine learning algorithms based on structured and unstructured data of a Swiss home care institution

Abstract

Home care institutions provide medical and social services to patients at home. Due to their ailments, recipients of home care services are at risk of requiring a hospital referral or experiencing early death. Early identification of a change in a patient's condition may support home care institutions to prevent such events and adjust their resource planning accordingly. Various structured and unstructured data are collected during the care pathway of a home care patient. Data mining techniques can be used to identify a patient at risk for specific events. Multiple studies have successfully applied data mining-based prediction models in different medical settings. In home care, medical decisions are currently based on anecdotal evidence rather than on evidence derived from data, which may lead to biases and errors. This master thesis aimed to predict if a patient recovers, must be relocated, remains unchanged, or dies within the next three months to identify the need for care intensification or reduction and improve home care institutions' planning capabilities.