Information extraction for populating lung cancer clinical research data

Liwei Wang; Lei Luo; Yanshan Wang; Jason A. Wampfler; Ping Yang; Hongfang Liu

doi:10.1109/ICHI.2019.8904601

Information extraction for populating lung cancer clinical research data

Liwei Wang, Lei Luo, Yanshan Wang, Jason A. Wampfler, Ping Yang, Hongfang Liu

Quantitative Health Sciences

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

1 Scopus citations

Abstract

Lung cancer is the second most common cancer and the wide adoption of electronic health records (EHRs) offers a potential of accelerating cohort-related epidemiological studies using informatics approaches. In this study, we developed and evaluated a natural language processing (NLP) system to extract information on stage, histology, grade and therapies (chemotherapy, radiotherapy and surgery) automatically for lung cancer patients from clinical narratives including clinical notes, pathology reports and surgery reports. Evaluation showed promising results with the recalls for stage, histology, grade, and therapies achieving 89%, 98%, 80%, and 100% respectively and the precisions were 71%, 89%, 90%, and 100% respectively. This study demonstrated the feasibility and accuracy of extracting related information from clinical narratives for lung cancer research.

Original language	English (US)
Title of host publication	2019 IEEE International Conference on Healthcare Informatics, ICHI 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538691380
DOIs	https://doi.org/10.1109/ICHI.2019.8904601
State	Published - Jun 2019
Event	7th IEEE International Conference on Healthcare Informatics, ICHI 2019 - Xi'an, China Duration: Jun 10 2019 → Jun 13 2019

Publication series

Name	2019 IEEE International Conference on Healthcare Informatics, ICHI 2019

Conference

Conference	7th IEEE International Conference on Healthcare Informatics, ICHI 2019
Country/Territory	China
City	Xi'an
Period	6/10/19 → 6/13/19

Keywords

Grade
Histology
Lung cancer
Natural language processing
Stage
Treatments

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Health Informatics
Biomedical Engineering

Access to Document

10.1109/ICHI.2019.8904601

Cite this

Wang, L., Luo, L., Wang, Y., Wampfler, J. A., Yang, P., & Liu, H. (2019). Information extraction for populating lung cancer clinical research data. In 2019 IEEE International Conference on Healthcare Informatics, ICHI 2019 [8904601] (2019 IEEE International Conference on Healthcare Informatics, ICHI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI.2019.8904601

Information extraction for populating lung cancer clinical research data. / Wang, Liwei; Luo, Lei; Wang, Yanshan et al.
2019 IEEE International Conference on Healthcare Informatics, ICHI 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8904601 (2019 IEEE International Conference on Healthcare Informatics, ICHI 2019).

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

Wang, L, Luo, L, Wang, Y, Wampfler, JA, Yang, P & Liu, H 2019, Information extraction for populating lung cancer clinical research data. in 2019 IEEE International Conference on Healthcare Informatics, ICHI 2019., 8904601, 2019 IEEE International Conference on Healthcare Informatics, ICHI 2019, Institute of Electrical and Electronics Engineers Inc., 7th IEEE International Conference on Healthcare Informatics, ICHI 2019, Xi'an, China, 6/10/19. https://doi.org/10.1109/ICHI.2019.8904601

Wang L, Luo L, Wang Y, Wampfler JA, Yang P, Liu H. Information extraction for populating lung cancer clinical research data. In 2019 IEEE International Conference on Healthcare Informatics, ICHI 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8904601. (2019 IEEE International Conference on Healthcare Informatics, ICHI 2019). doi: 10.1109/ICHI.2019.8904601

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abstract = "Lung cancer is the second most common cancer and the wide adoption of electronic health records (EHRs) offers a potential of accelerating cohort-related epidemiological studies using informatics approaches. In this study, we developed and evaluated a natural language processing (NLP) system to extract information on stage, histology, grade and therapies (chemotherapy, radiotherapy and surgery) automatically for lung cancer patients from clinical narratives including clinical notes, pathology reports and surgery reports. Evaluation showed promising results with the recalls for stage, histology, grade, and therapies achieving 89%, 98%, 80%, and 100% respectively and the precisions were 71%, 89%, 90%, and 100% respectively. This study demonstrated the feasibility and accuracy of extracting related information from clinical narratives for lung cancer research.",

keywords = "Grade, Histology, Lung cancer, Natural language processing, Stage, Treatments",

author = "Liwei Wang and Lei Luo and Yanshan Wang and Wampfler, {Jason A.} and Ping Yang and Hongfang Liu",

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doi = "10.1109/ICHI.2019.8904601",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

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AB - Lung cancer is the second most common cancer and the wide adoption of electronic health records (EHRs) offers a potential of accelerating cohort-related epidemiological studies using informatics approaches. In this study, we developed and evaluated a natural language processing (NLP) system to extract information on stage, histology, grade and therapies (chemotherapy, radiotherapy and surgery) automatically for lung cancer patients from clinical narratives including clinical notes, pathology reports and surgery reports. Evaluation showed promising results with the recalls for stage, histology, grade, and therapies achieving 89%, 98%, 80%, and 100% respectively and the precisions were 71%, 89%, 90%, and 100% respectively. This study demonstrated the feasibility and accuracy of extracting related information from clinical narratives for lung cancer research.

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KW - Histology

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Information extraction for populating lung cancer clinical research data

Abstract

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Conference

Keywords

ASJC Scopus subject areas

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