Classifier chains for positive unlabelled multi-label learning

The comparative experimental study of rehabilitation program decision for spinal cord injury based on electronic medical records

Objective

Electronic medical records (EMRs) contain patients' medical and health information. The Utilization of EMRs for assisted diagnosis is of significant importance for the rehabilitation of spinal cord injury (SCI) patients. Therefore, this study proposes a decision-making model for rehabilitation programs of SCI patients based on EMRs.

Methods

First, an Electronic Medical Records (EMR) dataset comprising 1252 Spinal Cord Injury (SCI) patients was constructed, and data preprocessing was completed. Second, the Random Forest (RF) feature extraction algorithm was utilized to select case features with high contribution levels. Then, to address the imbalance issue in EMRs, a multi-label learning framework based on the improved MLSMOTE was adopted. Finally, seven multi-label classification models were employed to predict patients' physical therapy (PT) prescriptions.

Results

The proposed improved MLSMOTE multi-label learning framework can solve the problem of class imbalance. Compared with the other six models, the CC model has improved significantly in many metrics. Its hamming loss and ranking loss were 0.1388 and 0.2467, and precision, recall, and F1-score were 83.33 %, 81.20 %, and 79.82 % respectively.

Conclusions

The improved MLSMOTE multi-label learning framework proposed in this study can make full use of the information in EMRs and effectively improve the decision-making accuracy of rehabilitation treatment programs.