论文标题
从自由文本电子健康记录中预测临床意图
Predicting Clinical Intent from Free Text Electronic Health Records
论文作者
论文摘要
经过患者咨询后，临床医生确定了患者管理的步骤。例如，临床医生可能会要求再次查看患者或将其转介给专家。虽然大多数临床医生会在患者的临床注释中记录其意图为“下一步”，但在某些情况下，临床医生可能会忘记表明其目的是作为命令或要求，例如未能下达后续顺序。因此，这会导致患者丧失到伴随，在某些情况下可能导致不利后果。在本文中，我们训练机器学习模型，以检测临床医生与患者临床注释中患者进行跟进的意图。注释者系统地确定了22种可能的临床意图类型和注释的3000个减肥临床注意事项。注释过程显示出标记的数据中的类不平衡，我们发现只有足够的标记数据可以训练22个意图中的11个。我们使用数据来训练基于BERT的多标签分类模型，并报告了所有目的的以下平均准确度指标：宏观精确：0.91，宏观回报：0.90，宏F1：0.90。
After a patient consultation, a clinician determines the steps in the management of the patient. A clinician may for example request to see the patient again or refer them to a specialist. Whilst most clinicians will record their intent as "next steps" in the patient's clinical notes, in some cases the clinician may forget to indicate their intent as an order or request, e.g. failure to place the follow-up order. This consequently results in patients becoming lost-to-follow up and may in some cases lead to adverse consequences. In this paper we train a machine learning model to detect a clinician's intent to follow up with a patient from the patient's clinical notes. Annotators systematically identified 22 possible types of clinical intent and annotated 3000 Bariatric clinical notes. The annotation process revealed a class imbalance in the labeled data and we found that there was only sufficient labeled data to train 11 out of the 22 intents. We used the data to train a BERT based multilabel classification model and reported the following average accuracy metrics for all intents: macro-precision: 0.91, macro-recall: 0.90, macro-f1: 0.90.
