research-article

Rule-based extraction of family history information from clinical notes

Authors:

João Rafael Almeida,

Sérgio MatosAuthors Info & Claims

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

Pages 670 - 675

https://doi.org/10.1145/3341105.3374000

Published: 30 March 2020 Publication History

Abstract

One of the features of Electronic Health Records (EHR) is to store the patient clinical data. Despite the efforts to structure all this data, clinical reports and notes containing essential information about each patient are still stored in free text. Some of this information refers to the family's health history and may be highly relevance for diagnosis and prognosis. We proposed two methodologies to unify this knowledge and extract family history information from clinical notes using rule-based techniques in natural language processing (NLP). With these methods, we intend to collect the family members mentioned in the text as well as associations to diseases and living status. The proposed methods were evaluated into two stages, demonstrating F-scores of 0.72 and 0.74 for the discovery of family members and observations, and 0.62 and 0.52 for the detection of the family relations with the observations, and their living status. Our methodologies raised new strategies to automatically annotate large amounts of EHRs, facilitating the detection of comorbidities within family relations.

References

[1]

2019. n2c2 Shared-Task and Workshop Track 2: n2c2/OHNLP Track on Family History Extraction. https://n2c2.dbmi.hms.harvard.edu/track2. Accessed: 2019-09-26.

[2]

Robert Bill, Serguei Pakhomov, Elizabeth S Chen, Tamara J Winden, Elizabeth W Carter, and Genevieve B Melton. 2014. Automated extraction of family history information from clinical notes. In AMIA Annual Symposium Proceedings, Vol. 2014. American Medical Informatics Association, 1709.

[3]

Hamish Cunningham. 2002. GATE, a general architecture for text engineering. Computers and the Humanities 36, 2 (2002), 223--254.

[4]

Richard S Dick, Elaine B Steen, Don E Detmer, et al. 1997. The computer-based patient record: an essential technology for health care. National Academies Press.

[5]

David Ferrucci and Adam Lally. 2004. UIMA: an architectural approach to unstructured information processing in the corporate research environment. Natural Language Engineering 10, 3-4 (2004), 327--348.

Digital Library

[6]

Jeff Friedlin and Clement J McDonald. 2006. Using a natural language processing system to extract and code family history data from admission reports. In AMIA Annual Symposium Proceedings, Vol. 2006. American Medical Informatics Association, 925.

[7]

Sergey Goryachev, Hyeoneui Kim, and Qing Zeng-Treitler. 2008. Identification and extraction of family history information from clinical reports. In AMIA Annual Symposium Proceedings, Vol. 2008. American Medical Informatics Association, 247.

[8]

Alan E Guttmacher, Francis S Collins, Richard H Carmona, et al. 2004. The family history-more important than ever. New England Journal of Medicine 351 (2004), 2333--2336.

[9]

Rita Kukafka, Jessica S Ancker, Connie Chan, John Chelico, Sharib Khan, Selasie Mortoti, Karthik Natarajan, Kempton Presley, and Kayann Stephens. 2007. Redesigning electronic health record systems to support public health. Journal of biomedical informatics 40, 4 (2007), 398--409.

Digital Library

[10]

Hongfang Liu, Suzette J Bielinski, Sunghwan Sohn, Sean Murphy, Kavishwar B Wagholikar, Siddhartha R Jonnalagadda, KE Ravikumar, Stephen T Wu, Iftikhar J Kullo, and Christopher G Chute. 2013. An information extraction framework for cohort identification using electronic health records. AMIA Summits on Translational Science Proceedings 2013 (2013), 149.

[11]

Edward Loper and Steven Bird. 2002. NLTK: the natural language toolkit. arXiv preprint cs/0205028 (2002).

Digital Library

[12]

Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014. The Stanford CoreNLP Natural Language Processing Toolkit. In Association for Computational Linguistics (ACL) System Demonstrations. 55--60. http://www.aclweb.org/anthology/P/P14/P14-5010

[13]

Sérgio Matos. 2018. Configurable web-services for biomedical document annotation. Journal of cheminformatics 10, 1 (2018), 68.

[14]

Sameer Pradhan, Noémie Elhadad, Wendy Chapman, Suresh Manandhar, and Guergana Savova. 2014. Semeval-2014 task 7: Analysis of clinical text. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). 54--62.

[15]

Yanshan Wang, Liwei Wang, Majid Rastegar-Mojarad, Sijia Liu, Feichen Shen, and Hongfang Liu. 2017. Systematic analysis of free-text family history in electronic health record. AMIA Summits on Translational Science Proceedings 2017 (2017), 104.

Cited By

de Oliveira JAntunes Rda Costa C(2024)SOAP classifier for free-text clinical notes with domain-specific pre-trained language modelsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.123046245:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.123046
Bhate NMittal AHe ZLuo X(2023)Zero-shot Learning with Minimum Instruction to Extract Social Determinants and Family History from Clinical Notes using GPT Model2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386811(1476-1480)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386811
Szekér SFogarassy GVathy-Fogarassy Á(2023)A general text mining method to extract echocardiography measurement results from echocardiography documentsArtificial Intelligence in Medicine10.1016/j.artmed.2023.102584143:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.artmed.2023.102584
Show More Cited By

Index Terms

Rule-based extraction of family history information from clinical notes
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
    2. Health informatics
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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cover image ACM Conferences

SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing

March 2020

2348 pages

ISBN:9781450368667

DOI:10.1145/3341105

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University
,
Tomas Cerny
Baylor University
,
Program Chairs:
Dongwan Shin
New Mexico Tech
,
Alessio Bechini
University of Pisa, Italy

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 30 March 2020

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SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing

March 30 - April 3, 2020

Brno, Czech Republic

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Cited By

de Oliveira JAntunes Rda Costa C(2024)SOAP classifier for free-text clinical notes with domain-specific pre-trained language modelsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.123046245:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.123046
Bhate NMittal AHe ZLuo X(2023)Zero-shot Learning with Minimum Instruction to Extract Social Determinants and Family History from Clinical Notes using GPT Model2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386811(1476-1480)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386811
Szekér SFogarassy GVathy-Fogarassy Á(2023)A general text mining method to extract echocardiography measurement results from echocardiography documentsArtificial Intelligence in Medicine10.1016/j.artmed.2023.102584143:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.artmed.2023.102584
Rybinski MDai XSingh SKarimi SNguyen A(2021)Extracting Family History Information From Electronic Health Records: Natural Language Processing AnalysisJMIR Medical Informatics10.2196/240209:4(e24020)Online publication date: 30-Apr-2021
https://doi.org/10.2196/24020
Almeida JSilva JMatos SOliveira J(2021)A two-stage workflow to extract and harmonize drug mentions from clinical notes into observational databasesJournal of Biomedical Informatics10.1016/j.jbi.2021.103849120:COnline publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1016/j.jbi.2021.103849
de Oliveira Jda Costa CAntunes R(2021)Data structuring of electronic health records: a systematic reviewHealth and Technology10.1007/s12553-021-00607-w11:6(1219-1235)Online publication date: 29-Oct-2021
https://doi.org/10.1007/s12553-021-00607-w
Almeida JSilva JSierra AMatos SOliveira J(2021)Leveraging Clinical Notes for Enhancing Decision-Making Systems with Relevant Patient InformationBiomedical Engineering Systems and Technologies10.1007/978-3-030-72379-8_26(521-540)Online publication date: 30-Mar-2021
https://doi.org/10.1007/978-3-030-72379-8_26
Silva JAlmeida JMatos S(2020)Extraction of Family History Information From Clinical Notes: Deep Learning and Heuristics ApproachJMIR Medical Informatics10.2196/228988:12(e22898)Online publication date: 29-Dec-2020
https://doi.org/10.2196/22898
Szekér SFogarassy GVathy-Fogarassy Á(undefined)A General Text Mining Method to Extract Echocardiography Measurement Results from Echocardiography DocumentsSSRN Electronic Journal10.2139/ssrn.3999264
https://doi.org/10.2139/ssrn.3999264

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