ArticleJapanese journal of comprehensive rehabilitation science2026
Predicting Motor FIM at Discharge for Recovery Stroke Patients in a Rehabilitation Ward Using an AI Predictive Analysis Tool.
Article in Japanese journal of comprehensive rehabilitation science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Morita K, Abe S, Ono K, Takahashi R, Ara Y, Abe M, Shirasaka T. Predicting Motor FIM at Discharge for Recovery Stroke Patients in a Rehabilitation Ward Using an AI Predictive Analysis Tool. Jpn J Compr Rehabil Sci 2026; 17: 40-45. Objective: This study aimed to create a prediction model for motor Functional Independence Measure (FIM) at discharge using an artificial intelligence (AI) predictive analysis tool (Prediction One, Sony Network Communications Inc.) and to clarify its accuracy. Methods: This study included 457 stroke patients discharged between April 2020 and March 2022 as the training dataset, and 43 stroke patients discharged between April and October 2022 as the validation dataset. First, an AI prediction model was developed using the training dataset. Next, the prediction accuracy was evaluated using the validation dataset. As part of the accuracy validation, the residuals between the actual motor FIM at discharge and the predicted values of the AI and therapists, as well as the correlation coefficients and coefficients of determination for each prediction result were calculated and statistically compared. Results: The coefficient of determination for the prediction model was 0.799. The residuals from the actual values were 5.43 ± 5.93 points for AI predictions and 6.42 ± 5.52 points for therapist predictions. The correlation coefficients and R Conclusion: The model developed in this study demonstrated an accuracy no less than that reported in previous studies. Furthermore, it achieved a predictive performance comparable to that of the therapists. This model could potentially predict motor FIM at discharge with consistent accuracy, regardless of the years of experience. In the future, we plan to expand the range of target conditions and assess their external validity.
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