论文标题
建立深度学习模型以预测ICU患者的死亡率
Building Deep Learning Models to Predict Mortality in ICU Patients
论文作者
论文摘要
重症监护病房中的死亡率预测被认为是有效治疗严重状况的患者的关键步骤之一。结果，已经开发了各种预测模型，以根据现代电子医疗保健记录解决此问题。但是，建模诸如时间序列变量之类的任务变得越来越具有挑战性，因为某些实验室测试结果（例如心率和血压）以不一致的时间频率采样。在本文中，我们建议使用与SAPS II分数相同的功能的几个深度学习模型。得出深入了解所提出的模型性能。基于众所周知的重症监护III的临床数据集医学信息MART进行了几项实验。预测结果证明了所提出的模型的能力，从精度，召回，F1分数和接收器操作特征曲线下的面积。
Mortality prediction in intensive care units is considered one of the critical steps for efficiently treating patients in serious condition. As a result, various prediction models have been developed to address this problem based on modern electronic healthcare records. However, it becomes increasingly challenging to model such tasks as time series variables because some laboratory test results such as heart rate and blood pressure are sampled with inconsistent time frequencies. In this paper, we propose several deep learning models using the same features as the SAPS II score. To derive insight into the proposed model performance. Several experiments have been conducted based on the well known clinical dataset Medical Information Mart for Intensive Care III. The prediction results demonstrate the proposed model's capability in terms of precision, recall, F1 score, and area under the receiver operating characteristic curve.
