用于不良结果预测的图表感知时间注意网络。

Zelalem Gero, Joyce C Ho
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引用次数: 0

摘要

各种医院和医疗中心越来越多地采用电子健康记录系统。这为利用自动化计算机系统协助医疗工作者提供了机会。其中利用最少,但丰富的病人信息来源是非结构化的临床文本。在这项工作中,我们开发了CATAN,一个图表感知的时间注意网络,用于从临床记录中学习患者表征。我们引入了一种新颖的表示,其中每个音符被认为是一个单独的单位,就像一个句子一样,由注意力加权的单词组成。这些笔记反过来又使用第二个加权单位——笔记注意力——聚合成患者的代表。与仅关注音符内容的标准注意力计算不同,我们将每个音符的图表时间作为注意力计算的约束。这使得我们的模型可以专注于更接近预测时间的音符。使用MIMIC-III数据集,我们通过经验表明,与各种最先进的1年死亡率预测和30天再入院基线相比,我们的患者代表性和注意力计算达到了最佳性能。此外,注意权重可以用来为我们的模型预测提供透明度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

CATAN: Chart-aware temporal attention network for adverse outcome prediction.

CATAN: Chart-aware temporal attention network for adverse outcome prediction.

There is an increased adoption of electronic health record systems by a variety of hospitals and medical centers. This provides an opportunity to leverage automated computer systems in assisting healthcare workers. One of the least utilized but rich source of patient information is the unstructured clinical text. In this work, we develop CATAN, a chart-aware temporal attention network for learning patient representations from clinical notes. We introduce a novel representation where each note is considered a single unit, like a sentence, and composed of attention-weighted words. The notes in turn are aggregated into a patient representation using a second weighting unit, note attention. Unlike standard attention computations which focus only on the content of the note, we incorporate the chart-time for each note as a constraint for attention calculation. This allows our model to focus on notes closer to the prediction time. Using the MIMIC-III dataset, we empirically show that our patient representation and attention calculation achieves the best performance in comparison with various state-of-the-art baselines for one-year mortality prediction and 30-day hospital readmission. Moreover, the attention weights can be used to offer transparency into our model's predictions.

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