Use of Machine-Learning Approaches to Predict Clinical Deterioration in Critically Ill Patients: A Systematic Review | Abstract

International Journal of Medical Research & Health Sciences (IJMRHS)
ISSN: 2319-5886 Indexed in: ESCI (Thomson Reuters)


Use of Machine-Learning Approaches to Predict Clinical Deterioration in Critically Ill Patients: A Systematic Review

Author(s):Tadashi Kamio, Tomoaki Van and Ken Masamune

Introduction: Early identification 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 identified 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 arti�?¯�?¬�?cial 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.

Select your language of interest to view the total content in your interested language

Scope Categories
  • Clinical Research
  • Epidemiology
  • Oncology
  • Biomedicine
  • Dentistry
  • Medical Education
  • Physiotherapy
  • Pulmonology
  • Nephrology
  • Gynaecology
  • Dermatology
  • Dermatoepidemiology
  • Otorhinolaryngology
  • Ophthalmology
  • Sexology
  • Osteology
  • Kinesiology
  • Neuroscience
  • Haematology
  • Psychology
  • Paediatrics
  • Angiology/Vascular Medicine
  • Critical care Medicine
  • Cardiology
  • Endocrinology
  • Gastroenterology
  • Infectious Diseases and Vaccinology
  • Hepatology
  • Geriatric Medicine
  • Bariatrics
  • Pharmacy and Nursing
  • Pharmacognosy and Phytochemistry
  • Radiobiology
  • Pharmacology
  • Toxicology
  • Clinical immunology
  • Clinical and Hospital Pharmacy
  • Cell Biology
  • Genomics and Proteomics
  • Pharmacogenomics
  • Bioinformatics and Biotechnology