Development and transfer learning of self-attention model for major adverse cardiovascular events prediction across hospitals.

ABSTRACT: Predicting major adverse cardiovascular events (MACE) is crucial due to its high readmission rate and severe sequelae. Current risk scoring model of MACE are based on a few features of a patient status at a single time point. We developed a self-attention-based model to predict MACE within 3 years from time series data utilizing numerous features in electronic medical records (EMRs). In addition, we demonstrated transfer learning for hospitals with insufficient data through code mapping and feature selection by the calculated importance using Xgboost. We established operational definitions and categories for diagnoses, medications, and laboratory tests to streamline scattered codes, enhancing clinical interpretability across hospitals. This resulted in reduced feature size and improved dat

SUBMITTER: Kim Y 

PROVIDER: S-EPMC11461710 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature