University of Southern California, Los Angeles, CA, USA.
Showa University School of Dentistry, Tokyo, Japan.
AMIA Annu Symp Proc. 2021 Jan 25;2020:943-952. eCollection 2020.
Physicians collect data in patient encounters that they use to diagnose patients. This process can fail if the needed data is not collected or if physicians fail to interpret the data. Previous work in orofacial pain (OFP) has automated diagnosis from encounter notes and pre-encounter diagnoses questionnaires, however they do not address how variables are selected and how to scale the number of diagnoses. With a domain expert we extract a dataset of 451 cases from patient notes. We examine the performance of various machine learning (ML) approaches and compare with a simplified model that captures the diagnostic process followed by the expert. Our experiments show that the methods are adequate to making data-driven diagnoses predictions for 5 diagnoses and we discuss the lessons learned to scale the number of diagnoses and cases as to allow for an actual implementation in an OFP clinic.
医生在患者就诊时收集数据，用于诊断患者。如果需要的数据未被收集，或者医生未能解释数据，这个过程可能会失败。先前在口腔颌面疼痛（OFP）方面的工作已经实现了从就诊记录和就诊前诊断问卷中自动诊断，但它们并未解决如何选择变量以及如何扩展诊断数量的问题。我们与一位领域专家一起从患者记录中提取了一个包含 451 个病例的数据集。我们检查了各种机器学习（ML）方法的性能，并与简化模型进行了比较，该模型捕捉了专家遵循的诊断过程。我们的实验表明，这些方法足以对 5 种诊断进行数据驱动的诊断预测，我们还讨论了为扩展诊断数量和病例数量以允许在 OFP 诊所中实际实施而获得的经验教训。
