开发和验证基于机器学习的查算法,以预测高风险的C型肝炎感染
Suk-Chan Jang1, Wei-Hsuan Lo-Ciganic2,3, Pilar Hernandez-Con1
1College of Pharmacy, University of Florida, Gainesville, Florida, USA.
Open forum infectious diseases
|August 28, 2025
概括
机器学习模型有效地识别出患有型肝炎病毒 (HCV) 感染的高风险个体. 梯度增强机模型表现出卓越的性能,为临床环境中针对性HCV查提供了有前途的工具.
科学领域:
- 公共卫生
- 传染性疾病
- 数据科学
背景情况:
- 美国的C型肝炎病毒 (HCV) 感染病例正在增加,
- 很大一部分HCV感染由于无症状而未被诊断出来.
- 需要有效的查工具来识别面临风险的人群.
研究的目的:
- 开发和验证用于预测和分层HCV感染风险的机器学习 (ML) 算法.
- 创建一个可以协助针对HCV的查策略的工具.
主要方法:
- 使用2016-2023年的OneFlorida+电子健康记录数据库.
- 包括18岁以上的个体进行HCV检测,分析了275个特征.
- 开发并验证了四种ML模型:弹性网,随机森林,梯度增强机和深度神经网络.
主要成果:
- 梯度提升机 (GBM) 模型获得了最高的性能 (C统计:0.916).
- GBM 显示了 79. 39% 的敏感度和 89. 08% 的特异性,每六次测试中发现一个阳性 HCV 病例.
- 在HCV患者中,前三个风险分值占90%以上.
结论:
- ML算法为预测和分层HCV感染风险提供了有效的方法.
- 这些模型是临床实践中针对性HCV查的一个有希望的工具.
- 这项研究强调了数据驱动方法在应对HCV等公共卫生挑战方面的潜力.
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