论文标题
电子健康记录中对临床实体分类的本体驱动的弱监督
Ontology-driven weak supervision for clinical entity classification in electronic health records
论文作者
论文摘要
在电子健康记录中，使用临床注释来识别诸如疾病及其时间性（例如，相对于时间指数的事件的顺序）等实体可以为许多重要的分析提供信息。但是，由于隐私问题，为临床实体任务创建培训数据是耗时和共享标记的数据的挑战。 COVID-19的信息需求强调了临床笔记训练机器学习模型的敏捷方法的需求。我们提出了Trove，这是使用医学本体和专家生成的规则进行弱监督实体分类的框架。与手工标记的音符不同，我们的方法易于共享和修改，同时提供与手动标记的培训数据学习相当的性能。在这项工作中，我们验证了六项基准任务的框架，并证明了Trove可以分析斯坦福医疗保健急诊室的患者记录的能力，以表明症状和风险因素。
In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e.g. the order of an event relative to a time index) can inform many important analyses. However, creating training data for clinical entity tasks is time consuming and sharing labeled data is challenging due to privacy concerns. The information needs of the COVID-19 pandemic highlight the need for agile methods of training machine learning models for clinical notes. We present Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules. Our approach, unlike hand-labeled notes, is easy to share and modify, while offering performance comparable to learning from manually labeled training data. In this work, we validate our framework on six benchmark tasks and demonstrate Trove's ability to analyze the records of patients visiting the emergency department at Stanford Health Care for COVID-19 presenting symptoms and risk factors.
