Meghana Gudala


pdf bib
Extracting Adherence Information from Electronic Health Records
Jordan Sanders | Meghana Gudala | Kathleen Hamilton | Nishtha Prasad | Jordan Stovall | Eduardo Blanco | Jane E Hamilton | Kirk Roberts
Proceedings of the 28th International Conference on Computational Linguistics

Patient adherence is a critical factor in health outcomes. We present a framework to extract adherence information from electronic health records, including both sentence-level information indicating general adherence information (full, partial, none, etc.) and span-level information providing additional information such as adherence type (medication or nonmedication), reasons and outcomes. We annotate and make publicly available a new corpus of 3,000 de-identified sentences, and discuss the language physicians use to document adherence information. We also explore models based on state-of-the-art transformers to automate both tasks.


pdf bib
Extraction of Lactation Frames from Drug Labels and LactMed
Heath Goodrum | Meghana Gudala | Ankita Misra | Kirk Roberts
Proceedings of the 18th BioNLP Workshop and Shared Task

This paper describes a natural language processing (NLP) approach to extracting lactation-specific drug information from two sources: FDA-mandated drug labels and the NLM Drugs and Lactation Database (LactMed). A frame semantic approach is utilized, and the paper describes the selected frames, their annotation on a set of 900 sections from drug labels and LactMed articles, and the NLP system to extract such frame instances automatically. The ultimate goal of the project is to use such a system to identify discrepancies in lactation-related drug information between these resources.