基于LSTM的肺结核预测模型在艾滋病毒感染患者中使用结构化电子医疗记录:一项回顾性机器学习研究
Jingfang Chen1,2, Linlin Liu3, Junxiong Huang1
1Faculty of Medicine, Macau University of Science and Technology, Macau, 999078, People's Republic of China.
Journal of multidisciplinary healthcare
|July 29, 2024
概括
这项研究使用机器学习来分析艾滋病毒患者记录,确定了16.7%的结核病共感染率. 深度学习模型准确地预测了共同感染,有助于早期发现和干预艾滋病毒和结核病.
科学领域:
- 人工智能在医学中的应用
- 机器学习用于公共卫生
- 流行病学数据分析 流行病学数据分析
背景情况:
- 人类免疫缺陷病毒 (HIV) 和结核病 (TB) 是具有广泛电子医疗记录的慢性传染病.
- 健康大数据的增长需要先进的数据提取和分析方法.
- 机器学习 (ML) 在医学研究中对于诊断,患者监测和流行病学研究至关重要.
研究的目的:
- 使用ML对艾滋病毒和结核病共感染的电子病历 (EMR) 进行结构化.
- 分析深艾滋病毒和结核病联合感染的流行病学特征和传播模式.
- 为早期检测和干预而开发艾滋病毒患者结核病联合感染的预测模型.
主要方法:
- 分析了来自4540名艾滋病毒患者 (2017-2021) 的纵向EMR数据.
- 采用微调的ChatGLM用于EMR结构和自然语言处理 (NLP) 来创建数据库.
- 利用多层感知器 (MLP) 和长短期记忆 (LSTM) 进行患者分类和结核病共感染预测.
主要成果:
- 聊天GLM实现了>0.95的症状提取精度.
- 在HIV患者中发现了16.7%的结核病联合感染率和25.1%的梅毒联合感染率.
- 对于结核病共感染,LSTM模型表现出强大的预测性能 (AUROC 0.827-0.850),表现优于MLP (AUROC 0.616-0.682).
结论:
- 结合MLP和LSTM,为EMR提取和疾病预测提供了先进的方法.
- 深度学习技术擅长管理结构化和非结构化医疗数据,用于疾病预测.
- 使用实验室时间序列数据的模型在预测结核病发病率方面明显优于仅使用EMR的模型.
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