An Interpretable Machine Learning Tool for In-Home Screening of Agitation Episodes in People Living with Dementia

Background Agitation affects around 30% of people living with dementia (PLwD), increasing carer burden and straining care services. Agitation screening typically relies on subjective clinical scales and direct patient observation, which are resource-intensive and challenging to incorporate into routine care. Clinical applicability of data-driven methods for agitation screening is limited by constraints such as short observational periods, data granularity, and lack of interpretability and generalisability. Current interventions for agitation are primarily medication-based, which may lead to severe side effects and lack personalisation. Understanding how real-world factors affect agitation within home settings offers a promising avenue towards identifying potential personalised non-pharmacological interventions. Methods We used longitudinal data (32,896 person-days from n=63 PLwD) collected using in-home monitoring devices. Employing machine learning techniques, we developed a screening tool to determine the weekly risk of agitation. We incorporated a traffic-light system for risk stratification to aid clinical decision-making and employed the SHapley Additive exPlanations (SHAP) framework to increase interpretability. We designed an interactive tool that enables the exploration of personalised non-pharmacological interventions, such as modifying ambient light and temperature. Results Light Gradient-boosting Machine (LightGBM) achieved the highest performance in identifying agitation with a sensitivity of 71.32±7.38% and specificity of 75.28±10.43%. Implementing the traffic-light system for risk stratification increased specificity by 15% and improved all metrics. Significant contributors to agitation included low nocturnal respiratory rate, heightened alertness during sleep, and increased indoor illuminance, as revealed by statistical and feature importance analysis. Using our interactive tool, we identified that adjusting indoor lighting levels and temperature were promising and feasible interventions within our cohort. Conclusions Our interpretable framework for agitation screening, developed using data from a dementia care study, showcases significant clinical value. The accompanying interactive interface allows for the in-silico simulation of non-pharmacological interventions, facilitating the design of personalised interventions that can improve in-home dementia care.