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首页> 外文期刊>BMC Medical Informatics and Decision Making >Time-sensitive clinical concept embeddings learned from large electronic health records
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Time-sensitive clinical concept embeddings learned from large electronic health records

机译：从大型电子健康记录中学习对时间敏感的临床概念嵌入

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摘要

Learning distributional representation of clinical concepts (e.g., diseases, drugs, and labs) is an important research area of deep learning in the medical domain. However, many existing relevant methods do not consider temporal dependencies along the longitudinal sequence of a patient’s records, which may lead to incorrect selection of contexts. To address this issue, we extended three popular concept embedding learning methods: word2vec, positive pointwise mutual information (PPMI) and FastText, to consider time-sensitive information. We then trained them on a large electronic health records (EHR) database containing about 50 million patients to generate concept embeddings and evaluated them for both intrinsic evaluations focusing on concept similarity measure and an extrinsic evaluation to assess the use of generated concept embeddings in the task of predicting disease onset. Our experiments show that embeddings learned from information within one visit (time window zero) improve performance on the concept similarity measure and the FastText algorithm usually had better performance than the other two algorithms. For the predictive modeling task, the optimal result was achieved by word2vec embeddings with a 30-day sliding window. Considering time constraints are important in training clinical concept embeddings. We expect they can benefit a series of downstream applications.
机译：学习临床概念（例如疾病，药物和实验室）的分布表示形式是医学领域深度学习的重要研究领域。但是，许多现有的相关方法并未考虑沿患者记录的纵向顺序的时间依赖性，这可能会导致错误选择上下文。为解决此问题，我们扩展了三种流行的概念嵌入学习方法：word2vec，正点向互信息（PPMI）和FastText，以考虑对时间敏感的信息。然后，我们在包含约5000万患者的大型电子健康记录（EHR）数据库中对其进行了培训，以生成概念嵌入，并对它们进行了评估（既侧重于概念相似性度量）又进行了外部评估，以评估在任务中使用生成的概念嵌入预测疾病发作。我们的实验表明，一次访问（零时间窗）内从信息中学习到的嵌入可提高概念相似性度量的性能，而FastText算法通常比其他两种算法具有更好的性能。对于预测建模任务，可通过具有30天滑动窗口的word2vec嵌入来获得最佳结果。考虑时间限制对训练临床概念嵌入很重要。我们希望它们可以使一系列下游应用受益。

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