Large Language Models and Artificial Neural Networks for Assessing 1-Year Mortality in Patients With Myocardial Infarction: Analysis From the Medical Information Mart for Intensive Care IV (MIMIC-IV) Database.

Journal: Journal of medical Internet research
PMID: 40354652

Abstract

BACKGROUND: Accurate mortality risk prediction is crucial for effective cardiovascular risk management. Recent advancements in artificial intelligence (AI) have demonstrated potential in this specific medical field. Qwen-2 and Llama-3 are high-performance, open-source large language models (LLMs) available online. An artificial neural network (ANN) algorithm derived from the SWEDEHEART (Swedish Web System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies) registry, termed SWEDEHEART-AI, can predict patient prognosis following acute myocardial infarction (AMI).

Authors

  • Boqun Shi
    Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Liangguo Chen
    Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Shuo Pang
    Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Yue Wang
    Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
  • Shen Wang
    Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Fadong Li
    Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Wenxin Zhao
    Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Pengrong Guo
    Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Leli Zhang
    Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Chu Fan
    Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Yi Zou
    Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Xiaofan Wu
    Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.

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