Use of Machine-Learning Approaches to Predict Clinical Deterioration in Critically Ill Patients: A Systematic Review
Journal Title: International Journal of Medical Research & Health Sciences (IJMRHS) - Year 2017, Vol 6, Issue 6
Abstract
Introduction: Early identifcation of patients with unexpected clinical deterioration is a matter of serious concern. Previous studies have shown that early intervention on a patient whose health is deteriorating improves the patient outcome, and machine-learning-based approaches to predict clinical deterioration may contribute to precision improvement. To date, however, no systematic review in this area is available. Methods: We completed a search on PubMed on January 22, 2017 as well as a review of the articles identifed by study authors involved in this area of research following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines for systematic reviews. Results: Twelve articles were selected for the current study from 273 articles initially obtained from the PubMed searches. Eleven of the 12 studies were retrospective studies, and no randomized controlled trials were performed. Although the artifcial neural network techniques were the most frequently used and provided high precision and accuracy, we failed to identify articles that showed improvement in the patient outcome. Limitations were reported related to generalizability, complexity of models, and technical knowledge. Conclusions: This review shows that machine-learning approaches can improve prediction of clinical deterioration compared with traditional methods. However, these techniques will require further external validation before widespread clinical acceptance can be achieved.
Authors and Affiliations
Tadashi Kamio| Department of Anesthesiology and Critical Care Medicine, Jichi Medical University Saitama Medical Center, Saitama, Japan, Institute of Advanced BioMedical Engineering and Science, Tokyo Women’s Medical University, Tokyo, Japan, Corresponding e-mail: tadashi-kamio@mail.goo.ne.jp, Tomoaki Van| Institute of Advanced BioMedical Engineering and Science, Tokyo Women’s Medical University, Tokyo, Japan, Ken Masamune| Institute of Advanced BioMedical Engineering and Science, Tokyo Women’s Medical University, Tokyo, Japan
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