Machine Learning-Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real-World Clinical and Image Data.

Journal: Journal of the American Heart Association
PMID: 39655759

Abstract

BACKGROUND: Machine learning (ML) techniques are widely employed across various domains to achieve accurate predictions. This study assessed the effectiveness of ML in predicting early mortality risk among patients with acute intracerebral hemorrhage (ICH) in real-world settings.

Authors

  • Koutarou Matsumoto
    Saiseikai Kumamoto Hospital, Kumamoto, Japan.
  • Kazuaki Ishihara
    Biostatistics Center Kurume University Kurume Japan.
  • Katsuhiko Matsuda
    Department of Radiology Saiseikai Kumamoto Hospital Kumamoto Japan.
  • Koki Tokunaga
    Department of Pharmacy Saiseikai Kumamoto Hospital Kumamoto Japan.
  • Shigeo Yamashiro
    Department of Neurosurgery, Saiseikai Kumamoto Hospital, Stroke Center.
  • Hidehisa Soejima
    Saiseikai Kumamoto Hospital, Kumamoto Japan.
  • Naoki Nakashima
    Medical Information Center, Kyushu University Hospital, Fukuoka, Japan.
  • Masahiro Kamouchi
    Department of Health Care Administration and Management, Graduate School of Medical Sciences Kyushu University Fukuoka Japan.

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