Deep Patient Representation of Clinical Notes via Multi-Task Learning for Mortality Prediction.
Deep Patient Representation of Clinical Notes via Multi-Task Learning for Mortality Prediction.
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发表时间：
2019
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通讯作者：
Yuqi Si;Kirk Roberts
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作者：
Yuqi Si;Kirk Roberts
我们提出了一种基于深度学习的多任务学习（MTL）架构，专注于从临床笔记中预测患者死亡率。MTL框架使模型能够学习概括为各种临床预测任务的患者表示。此外，我们演示了MTL如何在单个分类任务（例如，院内死亡率预测）简单地通过合并相关任务（例如，30-日和1年死亡率预测）纳入MTL框架。为了实现这一点，我们利用与MTL损失组件相关联的多级卷积神经网络（CNN）。该模型使用3、5和20个任务进行评估，并且始终能够产生比单任务学习（STL）分类器性能更高的模型。我们进一步讨论了多任务模型对其他感兴趣的临床结果的影响，包括能够产生高质量的表示，这些表示可以被更简单的模型发挥更大的作用。总的来说，这项研究证明了MTL在STL未能利用的任务中的效率和普遍性。
We propose a deep learning-based multi-task learning (MTL) architecture focusing on patient mortality predictions from clinical notes. The MTL framework enables the model to learn a patient representation that generalizes to a variety of clinical prediction tasks. Moreover, we demonstrate how MTL enables small but consistent gains on a single classification task (e.g., in-hospital mortality prediction) simply by incorporating related tasks (e.g., 30-day and 1-year mortality prediction) into the MTL framework. To accomplish this, we utilize a multi-level Convolutional Neural Network (CNN) associated with a MTL loss component. The model is evaluated with 3, 5, and 20 tasks and is consistently able to produce a higher-performing model than a single-task learning (STL) classifier. We further discuss the effect of the multi-task model on other clinical outcomes of interest, including being able to produce high-quality representations that can be utilized to great effect by simpler models. Overall, this study demonstrates the efficiency and generalizability of MTL across tasks that STL fails to leverage.