1. Development and transfer learning of self-attention model for major adverse cardiovascular events prediction across hospitals
- Author
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Yunha Kim, Heejun Kang, Hyeram Seo, Heejung Choi, Minkyoung Kim, JiYe Han, Gaeun Kee, Seohyun Park, Soyoung Ko, HyoJe Jung, Byeolhee Kim, Tae Joon Jun, Jae-Hyung Roh, and Young-Hak Kim
- Subjects
Cardiovascular disease ,Transfer learning ,Feature selection ,Risk prediction ,Multi-source data ,Medicine ,Science - Abstract
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 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.
- Published
- 2024
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