Objective:

To develop and validate a predictive model utilizing machine learning for assessing patients’ activities of daily living (ADL) recovery at 3 months post-stroke rehabilitation.

Approach:

Key Findings:

• The final prediction model (ADL-3 M) includes the Braden score, baseline BI score, and age.
• The AUC for ADL-3 M was 0.832 (95% CI: 0.779–0.885) in the training cohort and 0.866 (95% CI: 0.806–0.926) in the validation cohort.
• SHAP analysis indicated the Braden score as the most significant predictor for the 3-month outcome.

Interpretation:

The nomogram predicts ADL recovery in subacute stroke patients at 3 months post-rehabilitation using routine clinical data.

Limitations:

• The study is retrospective and may be subject to biases inherent in such designs.
• The model's applicability may be limited to similar clinical settings.

Conclusion:

The nomogram provides a tool for predicting ADL recovery.

Attribution Notice

This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.