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

Area of Science:

• Computational biology and data science applications in clinical medicine.
• Cardiovascular disease research and patient stratification.

Background:

• Accurate patient classification is crucial for effective treatment strategies and prognosis in acute coronary syndrome (ACS).
• Current disease classification methods may not fully capture the complexity of ACS patient heterogeneity.
• Data-driven approaches like cluster analysis offer potential for refined patient subtyping.

Purpose of the Study:

• To perform an outcome-driven cluster analysis on acute coronary syndrome (ACS) patients.
• To jointly consider patient treatment and outcomes for a more accurate patient state assessment.
• To identify distinct patient clusters within ACS for improved classification and subtyping.

Main Methods:

• Utilized an outcome-driven cluster analysis framework for ACS patient data.
• Employed a multi-task neural network with attention mechanisms for patient state learning, clustering, and feature importance.
• Integrated treatment and patient outcome data as key indicators for patient state.

Main Results:

• Discovered seven distinct patient clusters within the ACS cohort.
• Demonstrated significant differences in characteristics and risk profiles for in-hospital major adverse cardiac events among the identified clusters.
• Highlighted the utility of the multi-task neural network in uncovering patient heterogeneity.

Conclusions:

• Outcome-driven cluster analysis using multi-task neural networks shows promise for ACS patient classification.
• The identified patient clusters offer potential for informing personalized therapeutic strategies and prognostic assessments.
• This approach enhances data-driven disease subtyping for complex syndromes like ACS.