Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI).

Journal: European journal of medical research
PMID: 39054495

Abstract

BACKGROUND: Tuberculosis spondylitis (TS), commonly known as Pott's disease, is a severe type of skeletal tuberculosis that typically requires surgical treatment. However, this treatment option has led to an increase in healthcare costs due to prolonged hospital stays (PLOS). Therefore, identifying risk factors associated with extended PLOS is necessary. In this research, we intended to develop an interpretable machine learning model that could predict extended PLOS, which can provide valuable insights for treatments and a web-based application was implemented.

Authors

  • Parhat Yasin
    Department of Spine Surgery, First Affiliated Hospital of Xinjiang Medical University, Urumqi, China, 830054.
  • Yasen Yimit
    Department of Radiology, The First People's Hospital of Kashi (Kashgar) Prefecture, Xinjiang, China, 844000; Xinjiang Key Laboratory of Artificial Intelligence assisted Imaging Diagnosis, Kashi (Kashgar), China, 844000.
  • Xiaoyu Cai
  • Abasi Aimaiti
    Department of Anesthesiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, People's Republic of China.
  • Weibin Sheng
    Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, People's Republic of China.
  • Mardan Mamat
    Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, People's Republic of China. mardanmmtmx@163.com.
  • Mayidili Nijiati
    The First People's Hospital of Kashi, Xinjiang, China.

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