一个基于 GPT 的 EHR 建模系统,用于无监督检测新型疾病
Boran Hao1, Yang Hu1, William G Adams2
1Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA.
Journal of biomedical informatics
|August 9, 2024
概括
使用生成预训练变压器 (GPT) 的AI模型可以通过分析患者电子健康记录 (EHR) 来检测新型疾病并预测疫情. 这种人工智能在早期检测和个性化治疗规划方面补充了医生.
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
- 流行病学 流行病学
背景情况:
- 早期发现新型疾病和新兴疫情对公共卫生至关重要.
- 医生领导的异常检测可以通过先进的计算工具来增强.
- 电子健康记录 (EHR) 包含大量用于疾病模式识别的数据.
研究的目的:
- 开发基于人工智能 (AI) 的异常检测模型.
- 为了补充医生在医院内识别新型疾病病例的专业知识.
- 通过早期检测,防止新出现的传染病爆发.
主要方法:
- 开发了一个基于生成预训练变压器 (GPT) 的临床异常检测系统.
- 该系统使用实证风险最小化 (ERM) 模拟住院患者的电子健康记录 (EHR).
- 灵感来自大型语言模型 (LLM) 的方法被用于计算外分发 (OOD) 异常分数.
主要成果:
- 在无监督环境中,GPT模型预测了严重急性呼吸系统综合征冠状病毒2 (SARS-CoV-2) 住院病例,ROC曲线下面面积 (AUC) 为92.2%.
- 个体患者异常检测和死亡率预测的AUC达到78.3%和94.7%,优于线性模型.
- 该模型捕获了各种SARS-CoV-2临床轨迹,并提供了可解释的检测.
结论:
- 一个GPT模型可以通过分析EHR时间序列,准确地检测医院内新出现的疫情.
- 人工智能系统可以识别异常患者病例,结果偏离模型预测.
- GPT模型预测临床变量的能力有助于个性化治疗规划.
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