King Andrew J, Cooper Gregory F, Hochheiser Harry, Clermont Gilles, Visweswaran Shyam
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.
AMIA Annu Symp Proc. 2015 Nov 5;2015:1967-75. eCollection 2015.
Electronic medical records (EMRs) are capturing increasing amounts of data per patient. For clinicians to efficiently and accurately understand a patient's clinical state, better ways are needed to determine when and how to display EMR data. We built a prototype system that records how physicians view EMR data, which we used to train models that predict which EMR data will be relevant in a given patient. We call this approach a Learning EMR (LEMR). A physician used the prototype to review 59 intensive care unit (ICU) patient cases. We used the data-access patterns from these cases to train logistic regression models that, when evaluated, had AUROC values as high as 0.92 and that averaged 0.73, supporting that the approach is promising. A preliminary usability study identified advantages of the system and a few concerns about implementation. Overall, 3 of 4 ICU physicians were enthusiastic about features of the prototype.
电子病历（EMR）正在记录每位患者越来越多的数据。为了让临床医生高效、准确地了解患者的临床状况，需要更好的方法来确定何时以及如何展示电子病历数据。我们构建了一个原型系统，该系统记录医生查看电子病历数据的方式，我们用它来训练模型，以预测哪些电子病历数据在给定患者中是相关的。我们将这种方法称为学习型电子病历（LEMR）。一名医生使用该原型查看了59例重症监护病房（ICU）患者的病例。我们利用这些病例的数据访问模式来训练逻辑回归模型，在评估时，这些模型的曲线下面积（AUROC）值高达0.92，平均为0.73，这表明该方法很有前景。一项初步的可用性研究确定了该系统的优点以及一些关于实施的问题。总体而言，4名ICU医生中有3名对该原型的功能很感兴趣。
