Interpretable machine learning model for outcome prediction in patients with aneurysmatic subarachnoid hemorrhage.

Journal: Critical care (London, England)
PMID: 39833976

Abstract

BACKGROUND: Aneurysmatic subarachnoid hemorrhage (aSAH) is a critical condition associated with significant mortality rates and complex rehabilitation challenges. Early prediction of functional outcomes is essential for optimizing treatment strategies.

Authors

  • Masamichi Moriya
    Department of Anesthesiology and Critical Care Medicine, Yokohama City University School of Medicine, Yokohama, Kanagawa, Japan. moriya.mas.hi@yokohama-cu.ac.jp.
  • Kenji Karako
    Hepato-Biliary-Pancreatic Surgery Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Shogo Miyazaki
    Department of Acupuncture and Moxibustion, Faculty of Health Care, Teikyo Heisei University, Tokyo, Japan.
  • Shin Minakata
    Department of Rehabilitation Medicine, Akita University Hospital, Akita, Japan.
  • Shuhei Satoh
    Department of Rehabilitation, Akita Cerebrospinal and Cardiovascular Center, Akita, Japan.
  • Yoko Abe
    Department of Gastroenterology, Fukushima Medical University Aizu Medical Center.
  • Shota Suzuki
    Department of Medical Science and Cardiorenal Medicine, Yokohama City University Graduate School of Medicine, Yokohama, Japan; Department of Nephrology and Hypertension, Yokohama City University Medical Center, Yokohama, Japan.
  • Shohei Miyazato
    Department of Rehabilitation, Naha City Hospital, 2-31-1, Furujima, Naha city, Okinawa, Japan.
  • Hikaru Takara
    Department of Rehabilitation, Naha City Hospital, 2-31-1, Furujima, Naha city, Okinawa, Japan.

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