マサムネ ケン   MASAMUNE Ken
  正宗 賢
   所属   研究施設 研究施設
   職種   教授
論文種別 症例報告
言語種別 英語
査読の有無 査読あり
招待の有無 招待あり
表題 Use of Machine-Learning Approaches to Predict Clinical Deterioration in
Critically Ill Patients: A Systematic Review
掲載誌名 正式名:International Journal of Medical Research & Health Sciences
略  称:IJMRHS
ISSNコード:2319-5886
掲載区分国外
巻・号・頁 6(6),1-7頁
著者・共著者 KAMIO Tadashi†, VAN Tomoaki , MASAMUNE Ken
発行年月 2017/06
概要 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 artificial 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,
researchmap用URL http://www.ijmrhs.com/medical-research/use-of-machinelearning-approaches-to-predict-clinical-deterioration-in-critically-ill-patients-a-systematic-review.pdf