Clinical notes contain an extensive record of a patient's health status, such as smoking status or the presence of heart conditions. However, this detail is not replicated within the structured data of electronic health systems. Phenotyping, the extraction of patient conditions from free clinical text, is a critical task which supports avariety of downstream applications such as decision support and secondary use ofmedical records. Previous work has resulted in systems which are high performing but require hand engineering, often of rules. Recent work in pretrained contextualized language models have enabled advances in representing text for a variety of tasks. We therefore explore several architectures for modeling pheno-typing that rely solely on BERT representations of the clinical note, removing the need for manual engineering. We find these architectures are competitive with or outperform existing state of the art methods on two phenotyping tasks.

通过对临床记录进行语义学分析，可以帮助精确地提取出患者的病情，进而为医疗决策和医疗档案二次利用提供支持；本研究探索了几种基于BERT模型的病情提取机制，避免了传统的手工规则的繁琐操作，实验结果表明这种机制在病情识别上具有较高的性能，可适用于医学领域的实际应用。

改进上下文化神经语言模型的临床笔记表型描述