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在艾滋病毒感染者和未感染者中使用自然语言处理识别肝硬化症
Jessie Torgersen1,2, Melissa Skanderson3,4, Farah Kidwai-Khan3,4
1Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Hepatology communications
|June 19, 2024
概括
自然语言处理 (NLP) 在成像报告中准确识别了脂肪性肝病 (SLD). 该工具发现SLD在72.2%的退伍军人老龄化队列研究参与者中普遍存在,无论艾滋病毒状况如何.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
背景情况:
- 脂肪性肝病 (SLD) 是一个日益严重的健康问题,临床识别方法有限.
- 自然语言处理 (NLP) 为在大型临床数据集中系统识别SLD提供了一个潜在的解决方案.
- 这项研究验证了NLP算法用于在成像报告中检测SLD.
研究的目的:
- 在临床成像报告中验证NLP算法用于识别脂肪性肝病 (SLD).
- 将验证的NLP工具应用于大型队列,以评估SLD流行率及其与艾滋病毒状况的关联.
- 为了评估NLP在识别肝脏肥胖症方面的性能.
主要方法:
- 在包括肝脏的成像报告中,NLP算法根据关键词"脂肪","脂肪病"和"脂肪肝炎"等关键词识别了SLD.
- 对算法性能进行了验证,对800份报告进行了手动临床审查.
- 该NLP工具应用于26,706名退伍军人老龄化队列研究参与者的成像报告.
主要成果:
- 该NLP算法显示了100%的灵敏度和88.5%的SLD识别的积极预测值.
- 在72.2%的研究队列中发现了SLD,基于HIV状态没有显著差异.
- SLD与代谢并发症,酒精使用障碍和B/C型肝炎有关,但与艾滋病毒感染无关.
结论:
- NLP算法准确地识别了艾滋病毒感染者和未感染者的SLD.
- 这种NLP工具对于研究大群体肝硬化症的决定因素和后果是有价值的.
- 虽然仅限于放射学研究的患者,但NLP方法为SLD评估提供了强大的方法.
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