基于电子健康记录的算法来选美国疾病控制和预防中心1级遗传疾病:范围审查
William R Harris1, Marianna S Hernandez2, Khanh N H Ngo3
1Harvard Medical School, Boston, MA, 02115, United States.
Journal of the American Medical Informatics Association : JAMIA
|August 26, 2025
概括
电子健康记录 (EHR) 算法有望用于识别家族性高胆固醇血症 (FH) 等遗传疾病. 机器学习模型优于基于规则的模型,但对于遗传性乳腺和卵巢癌 (HBOC) 和林奇综合征需要更多的研究.
科学领域:
- 基因组医学
- 临床信息学
- 公共卫生基因组学
背景情况:
- 遗传性疾病的错误诊断,包括家族性高胆固醇血症 (FH),遗传性乳腺癌和卵巢癌 (HBOC),以及林奇综合征,构成了重大的临床挑战.
- 这些疾病被美国疾病控制和预防中心 (CDC) 指定为1级基因组应用,强调了它们对公共卫生的重要性.
研究的目的:
- 对基于电子健康记录 (EHR) 的算法用于识别患有FH,HBOC和林奇综合征的证据进行范围审查.
- 总结这些基于电子健康记录的算法的性能和实施结果.
主要方法:
- 根据JBI证据综合手册和PRISMA-ScR指南进行了全面的范围审查.
- 在Ovid MEDLINE,Embase和Web of Science进行了搜索,直到2024年10月,用于评估FH,HBOC或林奇综合征的EHR算法.
- 符合条件的研究集中在检测确诊病例的算法性能或未选定人群的实施结果上.
主要成果:
- 22项研究符合纳入标准,其中大多数 (20/22) 集中在FH上.
- 机器学习算法与基于规则的FH检测算法相比显示出更高的性能 (AUROC范围为0.78-0.95).
- 对FH的实施研究报告了11%至67%的积极预测值. 对HBOC和林奇综合征存在有限的证据 (两项研究),使用基于规则的低灵敏度算法.
结论:
- 基于临床标准识别遗传疾病的机器学习模型始终优于基于规则的算法.
- 虽然基于电子健康记录的查有助于早期识别疾病预防控制中心一级遗传疾病,但技术方面的进步和实施策略对于改善患者护理至关重要.
- 迫切需要进一步的研究来开发和验证HBOC和林奇综合征的有效EHR算法.
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