Integrating Social Determinants of Health in a Multi-Modal Deep Clustering Survival Model for Injury-Risk in Alzheimer's and Related Dementia Patients

As our population ages, the prevalence of Alzheimer's Disease and Related Dementias (ADRD) and its associated burdens continue to rise. Social Determinants of Health (SDOH) significantly influence both ADRD development and progression. Using Electronic Health Records (EHR) from a quaternary care academic medical center in a diverse urban setting, we investigated SDOH's impact on multi-modal deep clustering survival machines. Our findings revealed that SDOH improved model performance across feature selection methods (DeepCox roll-out vs. SHAP DeepExplainer) and EHR clinical modalities (medication vs. laboratory). Additionally, Laboratory features proved more informative than medications for predicting injury-fall risk. Our results highlight SDOH's crucial role in ADRD progression, particularly regarding injury-fall risk. We found that feature importance varied by selection method when analyzing multi-modality EHR data, with education emerging as a key SDOH factor among our top 10 features, underscoring its significance in ADRD progression.