Keisuke Ono, PT, BA, Ryosuke Takahashi, PT, MS, Kazuyuki Morita, OT, Advanced Diploma, Yosuke Ara, OT, BA, Senshu Abe, PT, MS, Soichirou Ito, PT, Advanced Diploma, Shogo Uno, PT, Advanced Diploma, Masayuki Abe, OT, BA, Tomohide Shirasaka, MD
Jpn J Compr Rehabil Sci 15: 1-7, 2024
Objective: This study aimed to develop a prediction
model for walking independence in patients with
stroke in the recovery phase at the time of hospital
discharge using Prediction One, an artificial
intelligence (AI)-based predictive analysis tool, and to
examine its utility.
Methods: Prediction One was used to develop a
prediction model for walking independence for 280
patients with stroke admitted to a rehabilitation wardbased
on physical and mental function information at
admission. In 134 patients with stroke hospitalized
during different periods, accuracy was confirmed by
calculating the correct response rate, sensitivity,
specificity, and positive and negative predictive values
based on the results of AI-based predictions and actual
results.
Results: The prediction accuracy (area under the
curve, AUC) of the proposed model was 91.7%. The
correct response rate was 79.9%, sensitivity was
95.7%, specificity was 62.5%, positive predictive
value was 73.6%, and negative predictive value was
93.5%.
Conclusion: The accuracy of the prediction model
developed in this study is not inferior to that of
previous studies, and the simplicity of the model
makes it highly practical.
Key words: AI, Prediction One, consequence prediction, walk, stroke