Challenges and Barriers in Applying Natural Language Processing to Medical Examiner Notes from Fatal Opioid Poisoning Cases

We detail the challenges and barriers in applying natural language processing techniques to a collection of medical examiner case investigation notes related to fatal opioid poisonings. Major advances in biomedical informatics have made natural language processing (NLP) of medical texts both a realistic and useful task. Biomedical NLP tools are typically designed to process documents originating from biomedical libraries or electronic health records (EHRs). The usefulness of biomedical NLP tools on texts authored outside of EHRs is unclear, despite an abundance of medicolegal documents existing at the intersection of medicine and law. In particular, we detail our experiences processing unstructured text and extracting semantic concepts using case investigation notes; these notes were authored by trained investigative professionals working in a medical examiner's office and describe cases containing deaths related to fatal opioid poisonings. Applying NLP to case notes is a particularly important step in generalizing the advances of biomedical NLP for other related domains and giving guidance to data scientists working with unstructured data generated outside of EHRs.