Interpretable Checklist for Delirium Detection

Delirium is a syndrome characterized by acute and fluctuating change in attention, awareness, and cognition. Common in older adults, especially those with underlying medical conditions, delirium is associated with higher mortality rates, longer hospital stays, and increased healthcare costs. Early identification and management of delirium therefore becomes essential in order to prevent adverse outcomes, improve patient health, and reduce healthcare costs. As multiple predisposing (e.g., neurological disorders) and precipitating factors (e.g., medications) can be involved in the aetiology of delirium, detecting the syndrome early on can be challenging. In this work, we present an interpretable multimodal checklist that can aid clinicians in delirium detection. Specifically, we leverage causal decision trees to extract most relevant features for delirium detection which are then used in learning the predictive checklist. Experiments demonstrate the efficacy of the approach over existing methods in terms of both detection accuracy and interpretability.