Deep Learning Identification of Asthma Inhaler Techniques in Clinical Notes.

Abstract

There are significant variabilities in clinicians' guideline-concordant documentation in asthma care. However, assessing clinicians' documentation is not feasible using only structured data but requires labor intensive chart review of electronic health records. Although the national asthma guidelines are available it is still challenging to use them as a real-time tool for providing feedback on adhering documentation guidelines for asthma care improvement. A certain guideline element, such as teaching or reviewing inhaler techniques, is difficult to capture by handcrafted rules since it requires contextual understanding of clinical narratives. This study examined a deep learning based natural language model, Bidirectional Encoder Representations from Transformers (BERT) coupled with distant supervision to identify inhaler techniques from clinical narratives. The BERT model with distant supervision outperformed the rule-based approach and achieved performance gain compared with the BERT without distant supervision.