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Cluster analysis aims at separating patients into phenotypically heterogenousgroups and defining therapeutically homogeneous patient subclasses. It is animportant approach in data-driven disease classification and subtyping. Acutecoronary syndrome (ACS) is a syndrome due to sudden decrease of coronary arteryblood flow, where disease classification would help to inform therapeuticstrategies and provide prognostic insights. Here we conducted outcome-drivencluster analysis of ACS patients, which jointly considers treatment and patientoutcome as indicators for patient state. Multi-task neural network withattention was used as a modeling framework, including learning of the patientstate, cluster analysis, and feature importance profiling. Seven patientclusters were discovered. The clusters have different characteristics, as wellas different risk profiles to the outcome of in-hospital major adverse cardiacevents. The results demonstrate cluster analysis using outcome-drivenmulti-task neural network as promising for patient classification andsubtyping.

Outcome-Driven Clustering of Acute Coronary Syndrome Patients using Multi-Task Neural Network with Attention