Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review.

Journal: International journal of medical informatics
PMID: 37156169

Abstract

OBJECTIVE: Disease comorbidity is a major challenge in healthcare affecting the patient's quality of life and costs. AI-based prediction of comorbidities can overcome this issue by improving precision medicine and providing holistic care. The objective of this systematic literature review was to identify and summarise existing machine learning (ML) methods for comorbidity prediction and evaluate the interpretability and explainability of the models.

Authors

  • Mohanad M Alsaleh
    Institute of Health Informatics, University College London, London, UK; Department of Health Informatics, College of Public Health and Health Informatics, Qassim University, Al Bukayriyah, Saudi Arabia. Electronic address: mohanad.alsaleh.21@ucl.ac.uk.
  • Freya Allery
    Institute of Health Informatics, University College London, London, UK.
  • Jung Won Choi
    Department of Neurosurgery, Brain Tumor Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
  • Tuankasfee Hama
    Institute of Health Informatics, University College London, London, UK.
  • Andrew McQuillin
    Division of Psychiatry, University College London, London, UK.
  • Honghan Wu
    University College London, London, United Kingdom.
  • Johan H Thygesen
    Division of Psychiatry, University College London, London, United Kingdom.

Keywords

External Resources

Popular Topics
Recent Journals