Multi-Modal Deep Clustering Survival Machines for Alzheimer's Disease Subtype Discovery

Survival clustering approaches estimate time-to-event prediction and clustering simultaneously. Current methods, however, are often limited by their reliance on single data modalities and their separation of risk prediction from patient subtyping. This creates a pressing need for a unified framework that can simultaneously discover patient subgroups and predict their conversion risk by integrating diverse biomarker data. Here, we introduce Multi-Modal Deep Clustering Survival Machines (MMDCSM), a unified framework that (1) encodes each modality via modality-specific MultiLayer Perceptrons (MLPs), (2) fuses embeddings into a joint representation, and (3) models survival outcomes through a mixture of Weibull expert distributions whose soft assignments simultaneously define patient subtypes and individualized survival curves. Applied to a cohort of 382 Mild Cognitive Impairment (MCI) patients, MMDCSM significantly outperformed existing methods in identifying distinct low- and high-risk subgroups for Alzheimer's disease (AD) conversion while delivering competitive accuracy in predicting each patient's personalized timeline to progression. Our model also identified key brain regions, including the hippocampus, that were most influential in distinguishing high-risk converters. This approach enables more accurate, early-stage risk stratification, paving the way for targeted interventions designed to delay or prevent disease progression.