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

Daniel R. Harris, Christian Eisinger, Yanning Wang, Chris Delcher

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
EditorsXintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
Pages3727-3736
Number of pages10
ISBN (Electronic)9781728162515
DOIs
StatePublished - Dec 10 2020
Event8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States
Duration: Dec 10 2020Dec 13 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference8th IEEE International Conference on Big Data, Big Data 2020
Country/TerritoryUnited States
CityVirtual, Atlanta
Period12/10/2012/13/20

Bibliographical note

Funding Information:
The project described was supported by the NIH National Center for Advancing Translational Sciences through grant number UL1TR001998.

Publisher Copyright:
© 2020 IEEE.

Keywords

  • data analysis
  • natural language processing
  • text processing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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