应用计算机文本挖掘算法在NCI护理模式研究的医疗记录中过量采样瘤突变状态
Benmei Liu1, Jennifer Stevens2, Gary Beverungen2
1Division of Cancer Control and Population Sciences, National Cancer Institute, NIH, Rockville, MD, USA.
International journal of medical informatics
|July 22, 2023
概括
文字挖掘算法有效地确定了国家癌症研究所 (NCI) 护理模式研究的非小细胞肺癌 (NSCLC) 患者的EGFR/ALK突变状态. 这使得对具有罕见突变的患者进行了至关重要的过量采样.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 国家癌症研究所 (NCI) 进行护理模式 (POC) 研究,以收集超出监测,流行病学和最终结果 (SEER) 计划的详细癌症治疗数据.
- 2019年POC研究的重点是非小细胞肺癌 (NSCLC) 和黑色素瘤,旨在过量采样具有特定EGFR/ALK突变的患者.
研究的目的:
- 从SEER数据库中开发和验证用于识别NSCLC病例中的EGFR/ALK突变状态的文本挖掘算法.
- 为了实现2019年POC研究的分层采样,专注于具有特定遗传突变的患者.
主要方法:
- 开发了文本挖掘算法,以选NSCLC病例的SEER数据库记录.
- 算法识别了突变测试状态,允许基于注册,性别,种族/种族和突变结果进行分层抽样.
- 对算法性能进行了评估,使用对医学图表抽象数据的灵敏度,特异性和准确性.
主要成果:
- 分析了2,434名晚期NSCLC患者 (2017-2018年诊断) 的样本.
- 692例 (13.2%) 被确定为EGFR/ALK突变阳性.
- 文本挖掘算法实现了77.6%的灵敏度,90.8%的特异性和84.8%的整体准确性.
结论:
- 文本挖掘是一种有效的方法,用于在可访问电子医疗记录的研究中过量采样患有不常见疾病的患者.
- 2019年POC研究为分析癌症治疗和患者特征提供了有价值的数据,特别是EGFR/ALK阳性NSCLC病例.
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