Predicting Short-Term Mortality after Endovascular Aortic Repair Using Machine Learning-Based Decision Tree Analysis.

Journal: Annals of vascular surgery
PMID: 39580030

Abstract

BACKGROUND: Endovascular aneurysm repair (EVAR) has revolutionized the treatment of abdominal aortic aneurysms by offering a less invasive alternative to open surgery. Understanding the factors that influence patient outcomes, particularly for high-risk patients, is crucial. The aim of this study was to determine whether machine learning (ML)-based decision tree analysis (DTA), a subset of artificial intelligence, could predict patient outcomes by identifying complex patterns in data.

Authors

  • Toshiya Nishibe
    Department of Medical Informatics and Management, Hokkaido Information University, Ebetsu, Japan; Department of Cardiovascular Surgery, Tokyo Medical University, Tokyo, Japan. Electronic address: toshiyanishibe@yahoo.co.jp.
  • Tsuyoshi Iwasa
    Department of Medical Informatics and Management, Hokkaido Information University, Ebetsu, Japan.
  • Masaki Kano
    Department of Cardiovascular Surgery, Tokyo Medical University, Tokyo, Japan.
  • Shinobu Akiyama
    Department of Cardiovascular Surgery, Tokyo Medical University, Tokyo, Japan.
  • Toru Iwahashi
    Department of Orthopaedic Surgery, Graduate School of Medicine, Osaka University, 2-2 Yamada-oka, Suita, Osaka, 565-0871, Japan.
  • Shoji Fukuda
    Department of Cardiovascular Surgery, Tokyo Medical University, Tokyo, Japan.
  • Jun Koizumi
    Department of Radiology, Chiba University School of Medicine, Chiba, Japan.
  • Masayasu Nishibe
    Department of Surgery, Eniwa Midorino Clinic, Eniwa, Japan.

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