  Cohort studies are of significant importance in the field of healthcare
analysis. However, existing methods typically involve manual, labor-intensive,
and expert-driven pattern definitions or rely on simplistic clustering
techniques that lack medical relevance. Automating cohort studies with
interpretable patterns has great potential to facilitate healthcare analysis
but remains an unmet need in prior research efforts. In this paper, we propose
a cohort auto-discovery model, CohortNet, for interpretable healthcare
analysis, focusing on the effective identification, representation, and
exploitation of cohorts characterized by medically meaningful patterns.
CohortNet initially learns fine-grained patient representations by separately
processing each feature, considering both individual feature trends and feature
interactions at each time step. Subsequently, it classifies each feature into
distinct states and employs a heuristic cohort exploration strategy to
effectively discover substantial cohorts with concrete patterns. For each
identified cohort, it learns comprehensive cohort representations with credible
evidence through associated patient retrieval. Ultimately, given a new patient,
CohortNet can leverage relevant cohorts with distinguished importance, which
can provide a more holistic understanding of the patient's conditions.
Extensive experiments on three real-world datasets demonstrate that it
consistently outperforms state-of-the-art approaches and offers interpretable
insights from diverse perspectives in a top-down fashion.
