ArticleCureus2021
Deep Learning-Based Functional Independence Measure Score Prediction After Stroke in Kaifukuki (Convalescent) Rehabilitation Ward Annexed to Acute Care Hospital.
Article in Cureus, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Clinically interpretable prediction models of stroke functional outcomes: A national cohort study of adults in inpatient rehabilitation facilities in the US.Archives of physical medicine and rehabilitation · 2026Article
- Predicting Motor FIM at Discharge for Recovery Stroke Patients in a Rehabilitation Ward Using an AI Predictive Analysis Tool.Japanese journal of comprehensive rehabilitation science · 2026Article
- Artificial intelligence in rehabilitation: a review of clinical effectiveness, real-world performance, safety, and equity across modalities and settings.Frontiers in digital health · 2026Review
- Preliminary approach to creation of a prediction model for diagnosis of Sjögren's syndrome using radiomics and machine learning techniques on computed tomography images of the parotid glands.Imaging science in dentistry · 2025Article
- Can AI predict walking independence in patients with stroke upon admission to a recovery-phase rehabilitation ward?Japanese journal of comprehensive rehabilitation science · 2024Article
- Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation.Frontiers in rehabilitation sciences · 2022Article
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Authors and funding
16 authors.
Funding
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Abstract
Introduction Prediction models of functional independent measure (FIM) score after kaifukuki (convalescent) rehabilitation ward (KRW) are needed to decide the treatment strategies and save medical resources. Statistical models were reported, but their accuracies were not satisfactory. We made such prediction models using the deep learning (DL) framework, Prediction One (Sony Network Communications Inc., Tokyo, Japan). Methods Of the 559 consecutive stroke patients, 122 patients were transferred to our KRW. We divided our 122 patients' data randomly into halves of training and validation datasets. Prediction One made three prediction models from the training dataset using (1) variables at the acute care ward admission, (2) those at the KRW admission, and (3) those combined (1) and (2). The models' determination coefficients (R
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