Identifying best fall-related balance factors and robotic-assisted gait training attributes in 105 post-stroke patients using clinical machine learning models.
BACKGROUND: Despite the promising effects of robot-assisted gait training (RAGT) on balance and gait in post-stroke rehabilitation, the optimal predictors of fall-related balance and effective RAGT attributes remain unclear in post-stroke patients at a high risk of fall. OBJECTIVE: We aimed to deter...
| Publicado en: | NeuroRehabilitation Vol. 55; no. 1; pp. 1 - 11 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179399596&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179399596 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10538135 3RE jtl: NeuroRehabilitation issn: 10538135 maglogo: N pubinfo: dt: 2024 vid: 55 iid: 1 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 179399596 179051544 179399596 179399596 10.3233/NRE-240116 179399596 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Identifying best fall-related balance factors and robotic-assisted gait training attributes in 105 post-stroke patients using clinical machine learning models. aug: au: Kim, Heejun Shin, Jiwon Kim, Yunhwan Lee, Yongseok You, Joshua H. affil: Department of Physical Therapy, Sports Movement Artificial Robotics Technology (SMART) Institute, Yonsei University, Wonju, Korea sug: subj: Accidental Falls Risk Factors Gait Training Methods Robotics Algorithms Evaluation Stroke Rehabilitation Stroke Complications Therapeutic Exercise Machine Learning Prediction Models Risk Assessment Human Funding Source Male Female Adult Middle Age Aged Scales Task Performance and Analysis Barthel Index Decision Trees Support Vector Machine Logistic Regression Sensitivity and Specificity ROC Curve Random Forest Questionnaires Accidental Falls Balance, Postural Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: BACKGROUND: Despite the promising effects of robot-assisted gait training (RAGT) on balance and gait in post-stroke rehabilitation, the optimal predictors of fall-related balance and effective RAGT attributes remain unclear in post-stroke patients at a high risk of fall. OBJECTIVE: We aimed to determine the most accurate clinical machine learning (ML) algorithm for predicting fall-related balance factors and identifying RAGT attributes. METHODS: We applied five ML algorithms— logistic regression, random forest, decision tree, support vector machine (SVM), and extreme gradient boosting (XGboost)— to a dataset of 105 post-stroke patients undergoing RAGT. The variables included the Berg Balance Scale score, walking speed, steps, hip and knee active torques, functional ambulation categories, Fugl– Meyer assessment (FMA), the Korean version of the Modified Barthel Index, and fall history. RESULTS: The random forest algorithm excelled (receiver operating characteristic area under the curve; AUC = 0.91) in predicting balance improvement, outperforming the SVM (AUC = 0.76) and XGboost (AUC = 0.71). Key determinants identified were knee active torque, age, step count, number of RAGT sessions, FMA, and hip torque. CONCLUSION: The random forest algorithm was the best prediction model for identifying fall-related balance and RAGT determinants, highlighting the importance of key factors for successful RAGT outcome performance in fall-related balance improvement. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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