[논문]머신러닝을 활용한 뇌졸중 환자의 기능적 결과 예측: 체계적 고찰

배수영; 남상훈; 홍익표

doi:10.22683/tsnr.2022.11.4.023

머신러닝을 활용한 뇌졸중 환자의 기능적 결과 예측: 체계적 고찰
Predicting Functional Outcomes of Patients With Stroke Using Machine Learning: A Systematic Review 원문보기

Therapeutic science for rehabilitation = 재활치료과학, v.11 no.4, 2022년, pp.23 - 39

배수영 (연세대학교 일반대학원 작업치료학과) , , 남상훈 (연세대학교 일반대학원 작업치료학과) , 홍익표 (연세대학교 소프트웨어디지털헬스케어융합대학 작업치료학과)

초록
AI-Helper

목적 : 본 연구는 뇌졸중 환자의 기능적 결과를 예측하기 위한 인구통계학적 및 임상학적 특징과 머신러닝의 사용을 체계적으로 분석하고 요약하기 위해 수행되었다. 연구방법 : PubMed, CINAHL과 Web of Science를 사용하여 2010년부터 2021년 사이에 게재된 연구를 검색하였다. 주요 검색어는 "machine learning OR data mining AND stroke AND function OR prediction OR/AND rehabilitation"을 사용하였다. 뇌 이미지 처리 기법만을 분석한 연구, 딥러닝만 적용한 연구와 전체 본문을 열람할 수 없는 연구는 제외되었다. 결과 : 검색한 결과, 총 9편의 국내외 논문을 선정했다. 선정된 논문에서 가장 많이 사용된 머신러닝 알고리즘은 서포트 벡터 머신(support vector machine, 19.05%)과 랜덤포레스트(random forest, 19.05%)였다. 9개 중 7개의 연구에서 뇌졸중 환자의 기능을 예측하기 위해 중요하다고 추출된 변수를 결과로 제시했다. 그 결과, 5개(55.56%)의 연구에서 뇌졸중 환자의 기능을 예측하기 위해 환자의 임상적 특성이 아닌 modified ranking scale (mRS) 및 functional independence measure (FIM)과 같은 초기 또는 퇴원 평가 점수가 중요하다고 도출되었다. 결론 : 이 연구는 mRS 및 FIM과 같은 뇌졸중 환자의 초기 또는 퇴원 평가 점수가 임상적 특성보다 기능적 결과에 더 많은 영향을 미칠 수 있음을 나타냈다. 따라서, 뇌졸중 환자의 기능적 결과를 향상시키기 위한 최적의 중재를 개발하고 적용하기 위해서는 뇌졸중 환자의 초기 및 퇴원 시 기능적 결과를 평가하고 검토하는 것이 필요하다.

Abstract ▼ AI-Helper

Objective : To summarize clinical and demographic variables and machine learning uses for predicting functional outcomes of patients with stroke. Methods : We searched PubMed, CINAHL and Web of Science to identify published articles from 2010 to 2021. The search terms were "machine learning OR data mining AND stroke AND function OR prediction OR/AND rehabilitation". Articles exclusively using brain imaging techniques, deep learning method and articles without available full text were excluded in this study. Results : Nine articles were selected for this study. Support vector machines (19.05%) and random forests (19.05%) were two most frequently used machine learning models. Five articles (55.56%) demonstrated that the impact of patient initial and/or discharge assessment scores such as modified ranking scale (mRS) or functional independence measure (FIM) on stroke patients' functional outcomes was higher than their clinical characteristics. Conclusions : This study showed that patient initial and/or discharge assessment scores such as mRS or FIM could influence their functional outcomes more than their clinical characteristics. Evaluating and reviewing initial and or discharge functional outcomes of patients with stroke might be required to develop the optimal therapeutic interventions to enhance functional outcomes of patients with stroke.

주제어

표/그림 (8)

그림 Figure 1. Search Process
표 Table 1. Characteristics of Articles
표 Table 1. Characteristics of Articles (Continued)
표 Table 2. Machine Learning Algorithms Used for Classification/Prediction
표 Table 3. The Result of Performance in Each Machine Learning Algorithms
그림 Figure 2. Number of Criteria Reported in Each Article
표 Table 4. The Information of the First Author in Each Article
표 Table 4. The Information of the First Author in Each Article (Continued)

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표제어: PCR

동의어: Packet Collision Rate

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용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

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