预测和理解艾滋病毒护理脱离的增强语言模型:坦桑尼亚的一个案例研究
Research square
|May 19, 2025
概括
一个增强的AI模型准确地预测了那些有脱离艾滋病毒治疗风险的个人,改善了保留和支持UNAIDS目标. 这种人工智能为及时干预提供了对可修改的风险因素的见解.
科学领域:
- 人工智能在公共卫生中的作用
- 机器学习用于疾病管理管理
- 艾滋病毒/艾滋病研究研究
背景情况:
- 持续参与艾滋病毒护理和抗逆转录病毒疗法 (ART) 的坚持对于实现全球艾滋病毒/艾滋病目标至关重要.
- 脱离艾滋病毒护理仍然是一个重大挑战,特别是在撒哈拉以南非洲,阻碍了进展.
- 传统的机器学习模型在预测早期干预的护理脱离方面取得了有限的成功.
研究的目的:
- 开发和评估一种增强的大型语言模型 (LLM),用于预测有脱离艾滋病毒护理风险的个体.
- 通过使用人工智能驱动的洞察力,识别有助于脱离艾滋病毒护理的可修改风险因素.
- 通过预测分析,提高艾滋病毒护理的保留率,并支持UNAIDS 95-95-95目标.
主要方法:
- 一个预先训练的LLM (LLaMA 3.1) 通过使用坦桑尼亚国家艾滋病毒护理和治疗计划 (2018-2023) 的广泛电子医疗记录 (EMR) 进行了微调.
- 在内部和外部验证数据集中,AI模型被评估其预测ART非坚持,非抑制病毒载量和跟踪损失 (LTFU) 的能力.
- 模型性能与最先进的机器学习模型和零射击LLM进行了比较,艾滋病毒医生评估了临床相关性.
主要成果:
- 增强的LLM在预测验证数据集的不良结果方面明显优于传统的ML模型和零射击LLM.
- 该模型准确地确定了78% (内部) 和73% (外部) 的LTFU高风险个人在预测的前25%内.
- 注意力分析显示,LLM专注于与随访差距和ART坚持相关的关键词,医生评估证实了在92.3%的一致病例中临床相关性.
结论:
- 开发的基于LLM的AI模型在预测艾滋病毒护理脱离和识别风险人群方面表现出高准确性.
- 这种人工智能工具可以增强资源配置,促进有针对性的干预,改善患者留守率和推进UNAIDS目标.
- 将这种人工智能集成到临床工作流程中可以补充医疗保健提供者,使及时决策和改善艾滋病毒感染者的健康结果.
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