论文标题
学习临床概念，以预测进展到严重的共同19号的风险
Learning Clinical Concepts for Predicting Risk of Progression to Severe COVID-19
论文作者
论文摘要
随着COVID-19现在普遍存在，高危个体的识别至关重要。利用来自宾夕法尼亚州西南部主要医疗保健提供者的数据，我们开发了预测严重COVID-19进展的生存模型。在这项工作中，我们在依赖许多功能的更准确模型和依赖一些与临床医生直觉相一致的功能的模型之间面临一个权衡。使事情复杂化，许多EHR功能往往较低，从而降低了较小型号的准确性。在这项研究中，我们开发了两组高性能风险评分：（i）由所有可用功能构建的无约束模型； （ii）在训练风险预测因子之前先了解一小部分临床概念的管道。学到的概念提高了相应特征（C-Index 0.858 vs. 0.844）的性能，并在评估样本外（随后的时间段）时证明了（i）的改进。我们的模型优于先前的工作（C-Index 0.844-0.872 vs. 0.598-0.810）。
With COVID-19 now pervasive, identification of high-risk individuals is crucial. Using data from a major healthcare provider in Southwestern Pennsylvania, we develop survival models predicting severe COVID-19 progression. In this endeavor, we face a tradeoff between more accurate models relying on many features and less accurate models relying on a few features aligned with clinician intuition. Complicating matters, many EHR features tend to be under-coded, degrading the accuracy of smaller models. In this study, we develop two sets of high-performance risk scores: (i) an unconstrained model built from all available features; and (ii) a pipeline that learns a small set of clinical concepts before training a risk predictor. Learned concepts boost performance over the corresponding features (C-index 0.858 vs. 0.844) and demonstrate improvements over (i) when evaluated out-of-sample (subsequent time periods). Our models outperform previous works (C-index 0.844-0.872 vs. 0.598-0.810).
