Personalised need of care in an ageing society: The making of a prediction tool based on register data

Danish municipalities monitor older persons who are at high risk of declining health and would later need home care services. However, there is no established strategy yet on how to accurately identify those who are at high risk. Therefore, there is great potential to optimise the municipalities’ prevention strategies. Denmark’s comprehensive set of electronic population registers provide longitudinal data that cover individual and household socio-demographics and medical history. Using these data, we developed and applied recurrent neural networks to predict the risk of a need of care services in the future and thus identify individuals who would benefit the most from the municipalities’ prevention strategies. We compared our recurrent neural network model to prediction models based on Cox regression and Fine–Gray regression in terms of calibration and discrimination. Challenges for the prediction modelling were the competing risk of death and the longitudinal information on the registered life course data.