使用语义特征和响应指标对个性化医生建议的新方法:模型评估研究研究
Yingbin Zheng1, Yunping Cai2, Yiwei Yan1
1Biomedical Big Data Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen City, China.
JMIR human factors
|August 15, 2024
概括
一个新的患者-医生混合推 (PPHR) 模型通过使用语义特征和响应指标来更好地选择医生,通过将患者的问题与医生专业相匹配来改善在线医疗分组.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 越来越多地使用在线医疗服务,需要改进患者分拣系统.
- 传统的方法很难准确地将患者的查询与医生的专业知识相匹配.
- 开发先进的医生推算法对于有效的医疗保健准入至关重要.
研究的目的:
- 开发和验证一个患者-医生混合推 (PPHR) 模型.
- 通过语义特征和医生响应指标来提高分拣性能.
主要方法:
- 收集了646,383个基于网络的医疗咨询记录.
- 开发了用于患者问题和医生专业的语义特征.
- 整合了医生响应率,以完善候选人排名.
- 使用指标和医生反评估模型性能,比较Sentence-BERT和Doc2Vec.
主要成果:
- 该PPHR模型在14名推医生 (F1得分:76.25%) 实现了最佳性能.
- 排除医生特征和反应率显著降低了绩效指标.
- 与Doc2Vec (53.4%) 相比,句子-BERT显示了更高的平均命中率 (88.6%).
结论:
- 该PPHR模型有效地利用语义分析和响应指标.
- 通过增强的分拣,使患者能够更准确地识别合适的医生.
关键词:
人权主义者的人权主义者 PPHR这是一个PPHR模型.斯伯特·斯伯特 (SBERT SBERT) 是一个著名的作家.句子双向编码器来自变压器的表示患者和医生的混合推建议.智能分类系统是智能分类系统.文本分析 文本分析基于网络的医疗服务.更多相关视频
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