Deep Learning-Based Functional Independence Measure Score Prediction After Stroke in Kaifukuki (Convalescent) Rehabilitation Ward Annexed to Acute Care Hospital
| Autor: | Naoya Ishida, Norio Narita, Kengo Kinjo, Masahito Katsuki, Yoshimichi Sato, Ryuzaburo Kochi, Kenichi Yokota, Taketo Nishizawa, K. Sato, Dan Ozaki, Kokoro Kawamura, Wenting Jia, Shinya Shimabukuro, Ohmi Watanabe, Iori Yasuda, Siqi Cai |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
functional independent measure (fim)
medicine.medical_specialty Stroke patient medicine.medical_treatment Neurosurgery sony network communications inc. japan Functional Independence Measure score artificial intelligence (ai) deep learning (dl) Acute care medicine Stroke Rehabilitation business.industry Deep learning General Engineering Independent measure prediction one medicine.disease stroke prediction model Neurology Physical therapy Artificial intelligence Public Health kaifukuki (convalescent) rehabilitation ward (krw) business Medical costs |
| Zdroj: | Cureus |
| ISSN: | 2168-8184 |
| Popis: | 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 (R2), correlation coefficients (rs), and residuals were calculated using the validation dataset. Results Of the 122 patients, the median age was 71, length of stay (LOS) in acute care ward 23 (17-30) days, LOS in KRW 53 days, total FIM scores at the admission of KRW 85, those at discharge 108. The mean FIM gain and FIM efficiency were 19 and 0.417. All patients were discharged home. Model (1), (2), and (3)'s R2 were 0.794, 0.970, and 0.972. Their mean residuals between the predicted and actual total FIM scores were -1.56±24.6, -4.49±17.1, and -2.69±15.7. Conclusion Our FIM gain and efficiency were better than national averages of FIM gain 17.1 and FIM efficiency 0.187. We made DL-based total FIM score prediction models, and their accuracies were superior to those of previous statistically calculated ones. The DL-based FIM score prediction models would save medical costs and perform efficient stroke and rehabilitation medicine. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|