An accurate and detailed account of patient medications, including medication changes within the patient timeline, is essential for healthcare providers to provide appropriate patient care. Healthcare providers or the patients themselves may initiate changes to patient medication. Medication changes take many forms, including prescribed medication and associated dosage modification. These changes provide information about the overall health of the patient and the rationale that led to the current care. Future care can then build on the resulting state of the patient. This work explores the automatic extraction of medication change information from free-text clinical notes. The Contextual Medication Event Dataset (CMED) is a corpus of clinical notes with annotations that characterize medication changes through multiple change-related attributes, including the type of change (start, stop, increase, etc.), initiator of the change, temporality, change likelihood, and negation. Using CMED, we identify medication mentions in clinical text and propose three novel high-performing BERT-based systems that resolve the annotated medication change characteristics. We demonstrate that our proposed systems improve medication change classification performance over the initial work exploring CMED.
翻译:准确和详细地说明病人的药物情况,包括在病人时间框架内的药物变化,对于保健提供者提供适当的病人护理至关重要; 保健提供者或病人本身可以对病人的药物进行改变; 药品改变有多种形式,包括处方药物和相关的剂量改变; 这些改变提供了病人总体健康状况的信息和导致目前护理的理由; 未来的护理可以以病人的病情为基础; 这项工作探索从免费文本临床说明中自动提取药物改变信息; 环境医疗事件数据集(CMED)是一系列附有说明的临床说明,通过多种与变化有关的特点,包括变化的类型(开始、停止、增加等)、改变的发起者、时间性、改变的可能性和否定,说明药物在临床文本中提及,并提出三个新的高效BERT系统,解决附加说明的药物变化特点。 我们证明,我们提议的系统改进了药物变化分类工作在探索CMED的初步工作中的绩效。