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

doi:10.1109/BigData50022.2020.9378443

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

Pharmacy Practice and Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

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 language	English
Title of host publication	Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
Editors	Xintao 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
Pages	3727-3736
Number of pages	10
ISBN (Electronic)	9781728162515
DOIs	https://doi.org/10.1109/BigData50022.2020.9378443
State	Published - Dec 10 2020
Event	8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States Duration: Dec 10 2020 → Dec 13 2020

Publication series

Name	Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference	8th IEEE International Conference on Big Data, Big Data 2020
Country/Territory	United States
City	Virtual, Atlanta
Period	12/10/20 → 12/13/20

Bibliographical note

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

Access to Document

10.1109/BigData50022.2020.9378443

Cite this

Harris, D. R., Eisinger, C., Wang, Y., & Delcher, C. (2020). Challenges and Barriers in Applying Natural Language Processing to Medical Examiner Notes from Fatal Opioid Poisoning Cases. In X. Wu, C. Jermaine, L. Xiong, X. T. Hu, O. Kotevska, S. Lu, W. Xu, S. Aluru, C. Zhai, E. Al-Masri, Z. Chen, & J. Saltz (Eds.), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020 (pp. 3727-3736). Article 9378443 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). https://doi.org/10.1109/BigData50022.2020.9378443

Harris, Daniel R. ; Eisinger, Christian ; Wang, Yanning et al. / Challenges and Barriers in Applying Natural Language Processing to Medical Examiner Notes from Fatal Opioid Poisoning Cases. Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. editor / Xintao 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. 2020. pp. 3727-3736 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020).

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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.",

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author = "Harris, {Daniel R.} and Christian Eisinger and Yanning Wang and Chris Delcher",

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Harris, DR, Eisinger, C, Wang, Y & Delcher, C 2020, Challenges and Barriers in Applying Natural Language Processing to Medical Examiner Notes from Fatal Opioid Poisoning Cases. in X Wu, C Jermaine, L Xiong, XT Hu, O Kotevska, S Lu, W Xu, S Aluru, C Zhai, E Al-Masri, Z Chen & J Saltz (eds), Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020., 9378443, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, pp. 3727-3736, 8th IEEE International Conference on Big Data, Big Data 2020, Virtual, Atlanta, United States, 12/10/20. https://doi.org/10.1109/BigData50022.2020.9378443

Challenges and Barriers in Applying Natural Language Processing to Medical Examiner Notes from Fatal Opioid Poisoning Cases. / Harris, Daniel R.; Eisinger, Christian; Wang, Yanning et al.
Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. ed. / Xintao 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. 2020. p. 3727-3736 9378443 (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Challenges and Barriers in Applying Natural Language Processing to Medical Examiner Notes from Fatal Opioid Poisoning Cases

AU - Harris, Daniel R.

AU - Eisinger, Christian

AU - Wang, Yanning

AU - Delcher, Chris

PY - 2020/12/10

Y1 - 2020/12/10

N2 - 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.

AB - 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.

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BT - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

A2 - Wu, Xintao

A2 - Jermaine, Chris

A2 - Xiong, Li

A2 - Hu, Xiaohua Tony

A2 - Kotevska, Olivera

A2 - Lu, Siyuan

A2 - Xu, Weijia

A2 - Aluru, Srinivas

A2 - Zhai, Chengxiang

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Harris DR, Eisinger C, Wang Y, Delcher C. Challenges and Barriers in Applying Natural Language Processing to Medical Examiner Notes from Fatal Opioid Poisoning Cases. In Wu X, Jermaine C, Xiong L, Hu XT, Kotevska O, Lu S, Xu W, Aluru S, Zhai C, Al-Masri E, Chen Z, Saltz J, editors, Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. 2020. p. 3727-3736. 9378443. (Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020). doi: 10.1109/BigData50022.2020.9378443

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

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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