在痴呆症患者中使用语言模型预测住院资格
Thomas H McCoy1,2, Roy H Perlis1,2
1Center for Quantitative Health, Massachusetts General Hospital, Boston, Massachusetts, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|November 11, 2025
概括
像GPT-4o这样的大型语言模型 (LLM) 可以估计阿尔茨海默病和相关痴呆症 (ADRD) 患者的6个月死亡风险. 这项技术可以通过识别需要支持性服务的患者来帮助改善对宿舍护理的准入.
科学领域:
- 人工智能在医学中的应用
- 临床信息学 临床信息学
- 老年医学 老年医学
背景情况:
- 阿尔茨海默病和相关痴呆症 (ADRD) 的患病率正在上升,但住院护理的准入仍然是一个挑战.
- 电子健康记录 (EHR) 包含用于临床决策支持的宝贵数据.
- 大型语言模型 (LLM) 显示出从非结构化EHR数据中提取见解的潜力.
研究的目的:
- 评估GPT-4o,一个大型语言模型,在ADRD患者中估计6个月死亡风险的能力.
- 确定LLM产生的死亡风险预测是否可以帮助宿舍转诊决定.
主要方法:
- 从两个学术医疗中心对9872名被诊断患有ADRD的患者进行了分析.
- 利用GPT-4o直接从退院摘要中估计6个月死亡风险,而无需重新培训.
- 采用考克斯回归来评估GPT-4o预测与实际死亡率之间的关联.
主要成果:
- GPT-4o的预测有效地分层了6个月死亡风险 (日志排名p < 0.001,AUC = 0.79).
- 预测显示与死亡率有很强的关联 (调整后的危险比率 = 31.02,p < 0.001).
- 两家参与医疗中心的表现一致.
结论:
- 使用常规临床文档,GPT-4o可以准确地分层痴呆症患者的死亡风险.
- 这些LLM驱动的风险估计表明有望促进及时的临终关怀转诊决定.
- 需要进一步的前性研究来验证这些发现在临床实践中.
关键词:
阿尔茨海默氏症的疾病是阿尔茨海默氏症.临床决策支持 临床决策支持痴呆症护理 痴呆症护理电子健康记录 (EHR) 是一种电子医疗记录.生命终端的护理.临终关怀的资格.大型语言模型 (LLM)死亡率预测死亡率预测自然语言处理 (NLP)预测建模预测建模更多相关视频
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