在电子健康记录中利用非结构化数据来检测儿科药物使用的不良事件 - - 广泛的回顾
Su Golder1, Karen O'Connor2, Guillermo Lopez-Garcia3
1Department of Health Sciences, University of York, York, United Kingdom.
medRxiv : the preprint server for health sciences
|April 1, 2025
概括
使用电子健康记录 (EHR) 识别儿童的不良药物事件具有挑战性. 本综述强调了非结构化电子病历数据的有限研究和潜力,与自然语言处理 (NLP) 一起,以改善儿科药物监测.
科学领域:
- 药物监督 药物监督 药物监督
- 儿科药物安全 儿科药物安全
- 医疗信息学 医疗信息学
背景情况:
- 儿童的不良药物事件 (ADEs) 引发了严重的公共卫生问题.
- 关于检测和监测儿科ADEs的研究特别有限.
- 电子健康记录 (EHR) 包含有价值的非结构化数据,用于药物监督.
研究的目的:
- 对使用非结构化EHR数据来识别儿科ADE进行范围审查.
- 评估该领域的研究现状.
- 确定儿科药物监测的挑战和未来方向.
主要方法:
- 在六个主要数据库 (MEDLINE,Embase,IEEE Xplore等) 中进行系统搜索. 在2024年9月.
- 纳入标准用于识别使用EHR的儿科ADE检测相关研究.
- 对已识别的研究进行分析,重点关注数据来源,方法和发现.
主要成果:
- 存在很大的研究差距,在984个记录中,只有9项研究符合纳入标准.
- 非结构化的EHR数据显示了增强儿科药物监测的潜力.
- 传统的自然语言处理 (NLP) 方法在ADE提取的概括性和上下文解释方面存在局限性.
结论:
- 有必要进行更多的研究来识别使用EHR数据的儿科ADEs.
- 基于变压器的模型和大型语言模型 (LLM) 的进步可以克服当前NLP的挑战.
- 利用LLM提供了一个有前途的途径,可以在儿科药物监测中大规模利用EHR数据.
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