Department of Pediatrics, University of Chicago, Chicago, IL, United States of America.
Division of Critical Care Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, United States of America.
PLoS One. 2019 Jul 31;14(7):e0220640. doi: 10.1371/journal.pone.0220640. eCollection 2019.
Deep learning algorithms have achieved human-equivalent performance in image recognition. However, the majority of clinical data within electronic health records is inherently in a non-image format. Therefore, creating visual representations of clinical data could facilitate using cutting-edge deep learning models for predicting outcomes such as in-hospital mortality, while enabling clinician interpretability. The objective of this study was to develop a framework that first transforms longitudinal patient data into visual timelines and then utilizes deep learning to predict in-hospital mortality.
All adult consecutive patient admissions from 2008-2016 at a tertiary care center were included in this retrospective study. Two-dimensional visual representations for each patient were created with clinical variables on one dimension and time on the other. Predictors included vital signs, laboratory results, medications, interventions, nurse examinations, and diagnostic tests collected over the first 48 hours of the hospital stay. These visual timelines were utilized by a convolutional neural network with a recurrent layer model to predict in-hospital mortality. Seventy percent of the cohort was used for model derivation and 30% for independent validation. Of 115,825 hospital admissions, 2,926 (2.5%) suffered in-hospital mortality. Our model predicted in-hospital mortality significantly better than the Modified Early Warning Score (area under the receiver operating characteristic curve [AUC]: 0.91 vs. 0.76, P < 0.001) and the Sequential Organ Failure Assessment score (AUC: 0.91 vs. 0.57, P < 0.001) in the independent validation set. Class-activation heatmaps were utilized to highlight areas of the picture that were most important for making the prediction, thereby providing clinicians with insight into each individual patient's prediction.
We converted longitudinal patient data into visual timelines and applied a deep neural network for predicting in-hospital mortality more accurately than current standard clinical models, while allowing for interpretation. Our framework holds promise for predicting several important outcomes in clinical medicine.
深度学习算法在图像识别方面已经达到了与人类相当的性能。然而，电子健康记录中的大多数临床数据本质上是非图像格式。因此，创建临床数据的可视化表示形式可以促进使用最先进的深度学习模型来预测住院死亡率等结果，同时使临床医生能够进行解释。本研究的目的是开发一个框架，首先将纵向患者数据转换为可视化时间线，然后利用深度学习来预测住院死亡率。
本回顾性研究纳入了 2008 年至 2016 年期间某三级保健中心的所有连续成年患者入院。为每位患者创建二维可视化表示形式，其中一维为临床变量，另一维为时序。预测因子包括在住院期间的前 48 小时内收集的生命体征、实验室结果、药物、干预措施、护士检查和诊断测试。该卷积神经网络利用具有递归层模型的可视化时间线来预测住院死亡率。该队列的 70%用于模型推导，30%用于独立验证。在 115825 例住院患者中，2926 例（2.5%）发生院内死亡。我们的模型在独立验证集中预测住院死亡率明显优于改良早期预警评分（接受者操作特征曲线下的面积 [AUC]：0.91 与 0.76，P<0.001）和序贯器官衰竭评估评分（AUC：0.91 与 0.57，P<0.001）。类激活热图用于突出对预测最重要的图像区域，从而为临床医生提供每个患者预测的见解。
我们将纵向患者数据转换为可视化时间线，并应用深度神经网络来预测住院死亡率，其准确性优于当前的标准临床模型，同时还允许进行解释。我们的框架有望预测临床医学中的几个重要结果。
