  Cluster analysis aims at separating patients into phenotypically heterogenous
groups and defining therapeutically homogeneous patient subclasses. It is an
important approach in data-driven disease classification and subtyping. Acute
coronary syndrome (ACS) is a syndrome due to sudden decrease of coronary artery
blood flow, where disease classification would help to inform therapeutic
strategies and provide prognostic insights. Here we conducted outcome-driven
cluster analysis of ACS patients, which jointly considers treatment and patient
outcome as indicators for patient state. Multi-task neural network with
attention was used as a modeling framework, including learning of the patient
state, cluster analysis, and feature importance profiling. Seven patient
clusters were discovered. The clusters have different characteristics, as well
as different risk profiles to the outcome of in-hospital major adverse cardiac
events. The results demonstrate cluster analysis using outcome-driven
multi-task neural network as promising for patient classification and
subtyping.
