论文标题
使用卷积对具有多模式学习的医疗实体进行卷积改善临床结果预测
Improving Clinical Outcome Predictions Using Convolution over Medical Entities with Multimodal Learning
论文作者
论文摘要
早期预测患者死亡率和住院时间（LOS）对于挽救患者的生命和医院资源管理至关重要。电子健康记录（EHR）的可用性对医疗领域产生了巨大影响，并且已经看到了一些预测临床问题的作品。但是，由于稀疏和高维质的性质稀疏，许多研究并未从临床笔记中受益。在这项工作中，我们从临床笔记中提取医疗实体，并将其用作时间序列功能以改善我们的预测以外的其他功能。我们提出了基于卷积的多模式结构，该建筑不仅可以有效地学习患者的医疗实体和时间序列ICU信号，而且还使我们能够比较不同嵌入技术（例如Word2Vec，fastText）对医疗实体的影响。在实验中，我们提出的方法稳健地胜过所有其他基线模型，包括所有临床任务的不同多模式体系结构。该方法的代码可在https://github.com/tanlab/convolutionMedicalner中获得。
Early prediction of mortality and length of stay(LOS) of a patient is vital for saving a patient's life and management of hospital resources. Availability of electronic health records(EHR) makes a huge impact on the healthcare domain and there has seen several works on predicting clinical problems. However, many studies did not benefit from the clinical notes because of the sparse, and high dimensional nature. In this work, we extract medical entities from clinical notes and use them as additional features besides time-series features to improve our predictions. We propose a convolution based multimodal architecture, which not only learns effectively combining medical entities and time-series ICU signals of patients, but also allows us to compare the effect of different embedding techniques such as Word2vec, FastText on medical entities. In the experiments, our proposed method robustly outperforms all other baseline models including different multimodal architectures for all clinical tasks. The code for the proposed method is available at https://github.com/tanlab/ConvolutionMedicalNer.
