Acute myocardial infarction prognosis prediction with reliable and interpretable artificial intelligence system

• Published in: Journal of the American Medical Informatics Association : JAMIA 2024-06, Vol.31 (7), p.1540-1550
• Main Authors: Kim, Minwook, Kang, Donggil, Kim, Min Sun, Choe, Jeong Cheon, Lee, Sun-Hack, Ahn, Jin Hee, Oh, Jun-Hyok, Choi, Jung Hyun, Lee, Han Cheol, Cha, Kwang Soo, Jang, Kyungtae, Bong, WooR I, Song, Giltae, Lee, Hyewon
• Format: Article
• Language: English
• Subjects: Aged; Artificial Intelligence; Female; Humans; Male; Middle Aged; Myocardial Infarction - diagnosis; Myocardial Infarction - mortality; Prognosis; Registries; Reproducibility of Results; Republic of Korea; Research and Application
• ISSN: 1067-5027; 1527-974X; 1527-974X
• DOI: 10.1093/jamia/ocae114

Abstract Objective Predicting mortality after acute myocardial infarction (AMI) is crucial for timely prescription and treatment of AMI patients, but there are no appropriate AI systems for clinicians. Our primary goal is to develop a reliable and interpretable AI system and provide some valuable insights regarding short, and long-term mortality. Materials and methods We propose the RIAS framework, an end-to-end framework that is designed with reliability and interpretability at its core and automatically optimizes the given model. Using RIAS, clinicians get accurate and reliable predictions which can be used as likelihood, with global and local explanations, and “what if” scenarios to achieve desired outcomes as well. Results We apply RIAS to AMI prognosis prediction data which comes from the Korean Acute Myocardial Infarction Registry. We compared FT-Transformer with XGBoost and MLP and found that FT-Transformer has superiority in sensitivity and comparable performance in AUROC and F1 score to XGBoost. Furthermore, RIAS reveals the significance of statin-based medications, beta-blockers, and age on mortality regardless of time period. Lastly, we showcase reliable and interpretable results of RIAS with local explanations and counterfactual examples for several realistic scenarios. Discussion RIAS addresses the “black-box” issue in AI by providing both global and local explanations based on SHAP values and reliable predictions, interpretable as actual likelihoods. The system’s “what if” counterfactual explanations enable clinicians to simulate patient-specific scenarios under various conditions, enhancing its practical utility. Conclusion The proposed framework provides reliable and interpretable predictions along with counterfactual examples.