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Research on Multi-label Classification Method of Medical Prescription by Using AI ——Taking electronic medical records of spinal cord injury patients as an example
ZHAO Huimin, GUO Xin, QIE Botao, LIU Yuli
2025, 35(2):
22-27.
DOI: 10.16046/j.cnki.issn2096-5680.2025.02.005
Electronic medical records (EMRs) are compilation of data concerning a patient's medical history and health status. The utilization of EMRs is imperative in the diagnosis of patients with spinal cord injuries (SCI), thus necessitating the development of an EHR-based rehabilitation prescription model for SCI patients. First, a dataset of EMRs containing 1443 paraplegic SCI patients was constructed, and data preprocessing was completed accordingly. Second, to address the problem of imbalanced EMRs, a multi-label classification based on MLSMOTE (Multi-label Synthetic Minority Over-sampling Technique) was proposed framework. Finally, seven multilabel classification models are used to predict patients' Physical Therapy (PT) prescriptions. The proposed MLSMOTE multi-label classification framework has been shown to adequately address the problem of category imbalance.The experimental results demonstrate that the RAkEL model exhibits significant improvement in numerous metrics when compared to the other six models. Specifically, the Hamming loss and ranking loss were found to be 0.1482 and 0.2616, respectively, while the precision, recall, and F1 score were determined to be 82.04%, 81.0%, and 78.07%, respectively.The MLSMOTE multi-label classification framework proposed in this study has the capacity to fully utilize EMRs data and effectively enhance the precision of rehabilitation therapy prescriptions.
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