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),pp.1-7
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