HCET: Hierarchical Clinical Embedding With Topic Modeling on Electronic Health Records for Predicting Future Depression.
HCET: Hierarchical Clinical Embedding With Topic Modeling on Electronic Health Records for Predicting Future Depression.
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DOI：
10.1109/jbhi.2020.3004072
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发表时间：
2021-04
影响因子：
7.7
通讯作者：
Arnold CW
中科院分区：
文献类型：
--
作者：
Meng Y;Speier W;Ong M;Arnold CW
机器学习算法的最新发展使模型能够在使用电子健康记录（EHR）数据的医疗保健任务中表现出令人印象深刻的性能。然而，EHR数据的异构性和稀疏性仍然具有挑战性。在这项工作中，我们提出了一个模型，利用异构数据和地址稀疏表示诊断，程序和药物代码与时间层次临床嵌入结合主题建模（HCET）的临床笔记。HCET汇总了各种类别的EHR数据，并根据单个患者的医院就诊情况学习其固有结构。我们证明了这种方法在临床诊断前的不同时间点预测抑郁症的潜力。我们发现，HCET优于所有的基线方法，在精确召回曲线下面积（PRAUC）的最高改善为0.07。此外，在EHR数据模式中应用注意力权重显着提高了性能，以及通过揭示每个数据模式的相对权重来提高模型的可解释性。我们的研究结果表明，该模型能够利用异构EHR信息来预测抑郁症，这可能对未来的筛查和早期检测产生影响。
Recent developments in machine learning algorithms have enabled models to exhibit impressive performance in healthcare tasks using electronic health record (EHR) data. However, the heterogeneous nature and sparsity of EHR data remains challenging. In this work, we present a model that utilizes heterogeneous data and addresses sparsity by representing diagnoses, procedures, and medication codes with temporal Hierarchical Clinical Embeddings combined with Topic modeling (HCET) on clinical notes. HCET aggregates various categories of EHR data and learns inherent structure based on hospital visits for an individual patient. We demonstrate the potential of the approach in the task of predicting depression at various time points prior to a clinical diagnosis. We found that HCET outperformed all baseline methods with a highest improvement of 0.07 in precision-recall area under the curve (PRAUC). Furthermore, applying attention weights across EHR data modalities significantly improved the performance as well as the model’s interpretability by revealing the relative weight for each data modality. Our results demonstrate the model’s ability to utilize heterogeneous EHR information to predict depression, which may have future implications for screening and early detection.