Why the study?
Current MACE risk scoring models rely on few features at a single time point despite the critical need to predict events given high readmission rates and severe sequelae.
Does a self-attention-based model with transfer learning improve the prediction of major adverse cardiovascular events within 3 years?
Does a self-attention-based model with transfer learning improve the prediction of major adverse cardiovascular events within 3 years?
A self-attention-based machine learning model utilizing transfer learning significantly improved the prediction of major adverse cardiovascular events across hospitals.
Answers are grounded in this paper and cite their sources.
May improve cross-hospital MACE prediction; hypothesis-generating and should not yet change practice.
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 data quality for transfer learning. The pre-trained model demonstrated an increase in AUROC after transfer learning, from 0.564 to 0.821. Furthermore, to validate the effectiveness of the predicted scores, we analyzed the data using traditional survival analysis, which confirmed an elevated hazard ratio for a group with high scores.
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Kim et al. (2024) studied this question.
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