使用自然语言处理电子医疗记录数据预测腹腔内伤害
Giovanna Danna1, Ravi Garg2, Joanna Buchheit2
1Chicago Medical School, Rosalind Franklin University, Chicago, IL.
Surgery
|July 7, 2024
概括
自然语言处理 (NLP) 可以从电子健康记录中预测腹腔内伤害. 这种工具与临床评估相结合,可以提高损伤识别的准确性,从而改善患者的治疗结果.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 创伤外科 手术 创伤外科
背景情况:
- 腹腔内损伤需要及时诊断才能有效治疗.
- 电子病历 (EMR) 包含大量的非结构化的临床笔记.
- 从EMR数据中预测腹腔内伤害可以帮助早期检测.
研究的目的:
- 开发和评估一种自然语言处理 (NLP) 模型,用于预测腹内损伤.
- 用EMRs的非结构化数据来检测损伤.
- 评估NLP在识别腹腔内损伤患者方面的表现.
主要方法:
- 在9个医院 (2015-2021) 中对受伤患者进行了回顾性观察队列研究.
- 从EMR中提取文字数据 (病史/体检,咨询,进展,放射学报告) 在治疗的前8小时内.
- 应用NLP技术 (词包,主要组件分析) 和机器学习模型 (逻辑回归,随机森林,梯度增强) 来预测腹内损伤.
主要成果:
- 总共采集了7000名患者接触的样本;2951人有足够的数据,其中84人 (2.9%) 确诊腹腔内伤害.
- 使用词袋和PCA的后勤回归实现了最高的预测性能 (AUC=0.9,回忆=0.73,精度=0.17).
- 关键的预测性文本特征包括"腹部"",骨盆"","和"血瘤".
结论:
- NLP展示了作为查腹内损伤的决策支持工具的潜力.
- 将NLP与人类临床评估相结合,可以优化损伤识别的精度.
- 这种方法可以改善创伤护理中的诊断工作流程和患者管理.
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