一个新的自动化算法来识别肺癌查从放射学订单的自由文本
Alison S Rustagi1,2, Marzieh Vali3,4, Francis J Graham4
1Center for Data to Discovery and Delivery Innovation (3DI), San Francisco VA Health Care System, San Francisco, CA, USA. Alison.rustagi@va.gov.
Journal of general internal medicine
|February 25, 2025
概括
一个自动化算法准确地识别了电子健康记录中的肺癌查 (LCS) 扫描. 该工具通过将查与诊断成像区分开来,提高LCS研究的可靠性.
科学领域:
- 医疗信息学 医疗信息学
- 放射学 放射学是一门学科.
- 公共卫生 公共卫生
背景情况:
- 对于无症状的个体,建议进行肺癌查 (LCS).
- 用于LCS的行政代码可能不准确地包括对症状患者的测试.
- 精确识别选试验对于公正的研究至关重要.
研究的目的:
- 验证一种自动化算法,用于在无症状患者中识别肺癌查 (LCS).
- 用电子健康数据区分查和诊断低剂量胸部CT扫描.
- 为了提高在大型患者群体中识别LCS的准确性.
主要方法:
- 使用当前程序术语 (CPT) 代码和从放射学订单中获取的自由文本开发并代地改进了一个算法.
- 分析了退伍军人卫生事务初级保健系统中65-80岁的4503名成年人的全国人口样本.
- 评估算法性能 (灵敏度,特异性,PPV,NPV) 与手动图表审查作为黄金标准.
主要成果:
- 该算法在识别符合LCS条件的个体时实现了97%的灵敏度和79%的特异性.
- 行政代码错误分类了31%的LCS扫描,而算法正确识别了95%的查扫描.
- 该算法在总人口中显示出高的正预测值 (90%) 和负预测值 (93%).
结论:
- 一个自动化的算法可以准确地区分查和诊断性胸部成像,这对于公正的非随机的LCS研究至关重要.
- 这种经过验证的算法提高了分析LCS结果的可靠性.
- 进一步的研究应该评估各种医疗保健系统中LCS的行政代码的准确性.
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