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Decision Tree Algorithm Identifies Stroke Patients Likely Discharge Home After Rehabilitation Using Functional and
Takeshi Imura1, Yuji Iwamoto2, Tetsuji Inagawa3
1Department of Rehabilitation, Faculty of Health Sciences, Hiroshima Cosmopolitan University, Hiroshima, Japan; Department of Rehabilitation, Araki Neurosurgical Hospital, Hiroshima, Japan.
Summary
Environmental and functional factors significantly impact home discharge for stroke survivors. House renovation, even with poor upper body dressing scores, strongly predicts successful home discharge after rehabilitation.
Area of Science:
- Rehabilitation Medicine
- Artificial Intelligence in Healthcare
- Stroke Recovery
Background:
- Environmental factors' role in stroke patient home discharge remains under-investigated.
- Limited research exists on using decision tree algorithms with diverse variables to predict home discharge for stroke patients.
Purpose of the Study:
- To identify key functional and environmental factors influencing home discharge after stroke inpatient rehabilitation.
- To apply machine learning, specifically decision tree algorithms, for predicting home discharge possibility.
Main Methods:
- A cohort study analyzed data from 1125 stroke patients admitted to a convalescence rehabilitation ward.
- Three Classification and Regression Tree (CART) models were developed to predict home discharge outcomes.
Main Results:
- The CART model integrating basic, functional, and environmental variables demonstrated the highest accuracy in predicting home discharge.
- Upper body dressing ability (Functional Independence Measure score) was a primary discriminator.
- House renovation emerged as a significant predictor of home discharge, even for patients with low dressing scores.
Conclusions:
- Decision tree algorithms effectively identified influential factors, including environmental aspects, for home discharge in convalescent stroke patients.
- Environmental modifications like house renovation play a crucial role in facilitating stroke survivors' return home.
