Steinberg Ethan, Jung Ken, Fries Jason A, Corbin Conor K, Pfohl Stephen R, Shah Nigam H
Stanford University, 450 Serra Mall, Stanford, CA 94305, USA.
Stanford University, 450 Serra Mall, Stanford, CA 94305, USA.
J Biomed Inform. 2021 Jan;113:103637. doi: 10.1016/j.jbi.2020.103637. Epub 2020 Dec 5.
Widespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes. However, this process is often constrained by having a relatively small number of patient records for training the model. We demonstrate that using patient representation schemes inspired from techniques in natural language processing can increase the accuracy of clinical prediction models by transferring information learned from the entire patient population to the task of training a specific model, where only a subset of the population is relevant. Such patient representation schemes enable a 3.5% mean improvement in AUROC on five prediction tasks compared to standard baselines, with the average improvement rising to 19% when only a small number of patient records are available for training the clinical prediction model.
电子健康记录（EHRs）的广泛采用推动了利用机器学习为各种临床结果构建预测模型的发展。然而，这一过程常常受到训练模型的患者记录数量相对较少的限制。我们证明，使用受自然语言处理技术启发的患者表示方案，可以通过将从全体患者群体中学到的信息转移到训练特定模型的任务中，提高临床预测模型的准确性，而在该特定模型中只有部分患者群体是相关的。与标准基线相比，这种患者表示方案在五个预测任务上使受试者工作特征曲线下面积（AUROC）平均提高了3.5%，当仅有少量患者记录可用于训练临床预测模型时，平均提高幅度升至19%。
