河北水利电力学院学报2025，Vol.35Issue(2)：22-27,6.DOI:10.16046/j.cnki.issn2096-5680.2025.02.005
AI用于医疗处方多标签分类方法研究
Research on Multi-label Classification Method of Medical Prescription by Using AI
摘要
Abstract
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'Phys-ical 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,respective-ly,while the precision,recall,and F1 score were determined to be 82.04%,81.0%,and 78.07%,respec-tively.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.关键词
电子病历/MLSMOTE/多标签分类/脊髓损伤Key words
electronic medical record/MLSMOTE/multi-label classification/spinal cord injury分类
信息技术与安全科学引用本文复制引用
赵慧敏,郭欣,郄博韬,刘玉丽..AI用于医疗处方多标签分类方法研究[J].河北水利电力学院学报,2025,35(2):22-27,6.基金项目
国家重点研发计划(2019YFB1312500) （2019YFB1312500）
