FABLE: A Semi-Supervised Prescription Information Extraction System.

Prescription information is an important component of electronic health records (EHRs). This information contains detailed medication instructions that are crucial for patients' well-being and is often detailed in the narrative portions of EHRs. As a result, narratives of EHRs need to be processed with natural language processing (NLP) methods that can extract medication and prescription information from free text. However, automatic methods for medication and prescription extraction from narratives face two major challenges: (1) dictionaries can fall short even when identifying well-defined and syntactically consistent categories of medication entities, (2) some categories of medication entities are sparse, and at the same time lexically (and syntactically) diverse. In this paper, we describe FABLE, a system for automatically extracting prescription information from discharge summaries. FABLE utilizes unannotated data to enhance annotated training data: it performs semi-supervised extraction of medication information using pseudo-labels with Conditional Random Fields (CRFs) to improve its understanding of incomplete, sparse, and diverse medication entities. When evaluated against the official benchmark set from the 2009 i2b2 Shared Task and Workshop on Medication Extraction, FABLE achieves a horizontal phrase-level F1-measure of 0.878, giving state-of-the-art performance and significantly improving on nearly all entity categories.