一个基于人工智能的模型,用于预测,预防和以患者为中心的方法,用于与代谢综合征相关的非传染性疾病
Alejandro Clarós1, Andreea Ciudin2,3, Jordi Muria1
1Higia.ai, Barcelona, Spain.
European journal of public health
|July 4, 2025
概括
与传统方法相比,变压器人工智能模型显著改善了代谢综合征 (MetS) 和相关疾病的早期检测. 这一进步有望改善患者管理,通过更快地识别未被诊断的病例,减少医疗保健负担.
科学领域:
- 医疗保健中的人工智能
- 计算医学是一种计算医学.
- 非传染性疾病的预测分析.
背景情况:
- 代谢综合征 (MetS) 是众多非传染性疾病 (NCD) 的前体,包括2型糖尿病 (T2D),代谢相关的脂肪性肝病 (MASLD),动脉动脉性脂质障碍症 (ATD) 和慢性病 (CKD).
- 目前的诊断和风险分层工具对于mets相关的非传染性疾病是不充分的,导致延迟干预,增加住院治疗和增加医疗保健支出.
- 对先进的预测模型的需求对于 MetS及其相关条件的积极管理至关重要.
研究的目的:
- 评估基于变压器的人工智能 (AI) 模型对 MetS 相关的非传染性疾病的预测性能.
- 将变压器AI与经典机器学习模型 (随机森林,线性SVC) 在识别MetS和相关条件方面的有效性进行比较.
- 量化人工智能驱动的早期检测对未被诊断的病例和潜在的医疗保健成本节省的影响.
主要方法:
- 在MIMIC-IV v2.2数据库中分析了183,958名患者的电子病历,使用了两阶段的变压器AI方法:预训练以捕获疾病模式并为特定预测进行微调.
- 对变压器模型与随机森林和线性SVC进行比较分析,使用曲线下的面积 (AUC) 和F1得分指标评估性能.
- 通过AI模型确定未被诊断的病例的量化和诊断延迟的评估及其与住院和医疗程序的相关性.
主要成果:
- 与经典机器学习方法相比,基于变压器的AI模型在所有评估的MetS相关的非传染性疾病中显示出明显优异的预测准确性 (更高的AUC值).
- 人工智能模型发现,CKD (2.58倍),T2D (0.78倍),脂质失调 (1.89倍),高血压 (3.33倍),MASLD (5.78倍) 和肥胖 (4.07倍) 的未被诊断病例大幅增加.
- 诊断延迟平均为90-500天,其中35%的错过的干预机会发生在前五次预约内,与住院病例增加84%和手术增加69%相关.
结论:
- 基于变压器的AI模型擅长捕捉复杂的时间性疾病模式,为MetS及其相关的NCD提供优越的预测能力,超过传统的机器学习方法.
- 将这些先进的人工智能模型集成到临床工作流中,可以促进可扩展,主动的MetS管理,并大大减少未诊断疾病的负担.
- 实施转换器人工智能可以优化资源配置,防止可避免的住院治疗,并最终通过使得早期和更准确的疾病检测,改善人口健康结果.
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