research-article

Challenges in understanding clinical notes: why NLP engines fall short and where background knowledge can help

Authors:

Sujan Perera,

Amit Sheth,

Krishnaprasad Thirunarayan,

Suhas Nair,

Neil ShahAuthors Info & Claims

DARE '13: Proceedings of the 2013 international workshop on Data management & analytics for healthcare

Pages 21 - 26

https://doi.org/10.1145/2512410.2512427

Published: 01 November 2013 Publication History

Get Access

Abstract

Understanding of Electronic Medical Records(EMRs) plays a crucial role in improving healthcare outcomes. However, the unstructured nature of EMRs poses several technical challenges for structured information extraction from clinical notes leading to automatic analysis. Natural Language Processing(NLP) techniques developed to process EMRs are effective for variety of tasks, they often fail to preserve the semantics of original information expressed in EMRs, particularly in complex scenarios. This paper illustrates the complexity of the problems involved and deals with conflicts created due to the shortcomings of NLP techniques and demonstrates where domain specific knowledge bases can come to rescue in resolving conflicts that can significantly improve the semantic annotation and structured information extraction. We discuss various insights gained from our study on real world dataset.

References

[1]

Alan R Aronson. Metamap: Mapping text to the umls metathesaurus. Bethesda, MD: NLM, NIH, DHHS, 2006.

Google Scholar

[2]

Olivier Bodenreider. The unified medical language system (umls): integrating biomedical terminology. Nucleic acids research, 32(suppl 1):D267--D270, 2004.

Google Scholar

[3]

Carol Friedman, Philip O Alderson, John HM Austin, James J Cimino, and Stephen B Johnson. A general natural-language text processor for clinical radiology. Journal of the American Medical Informatics Association, 1(2):161--174, 1994.

Crossref

Google Scholar

[4]

Sujan Perera, Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth, and Suhas Nair. Data driven knowledge acquisition method for domain knowledge enrichment in the healthcare. In IEEE International Conference on Bioinformatics and Biomedicine, pages 1--8, October 2012.

Digital Library

Google Scholar

[5]

Guergana K Savova, James J Masanz, Philip V Ogren, Jiaping Zheng, Sunghwan Sohn, Karin C Kipper-Schuler, and Christopher G Chute. Mayo clinical text analysis and knowledge extraction system (ctakes): architecture, component evaluation and applications. Journal of the American Medical Informatics Association, 17(5):507--513, 2010.

Crossref

Google Scholar

Cited By

View all

Trajanov DTrajkovski VDimitrieva MDobreva JJovanovik MKlemen MŽagar ARobnik-Šikonja M(2023)Review of Natural Language Processing in PharmacologyPharmacological Reviews10.1124/pharmrev.122.00071575:4(714-738)Online publication date: 17-Mar-2023
https://doi.org/10.1124/pharmrev.122.000715
Kim HPark YPark YChoi EKim SYou HBae Y(2023)Identifying Alcohol-Related Information From Unstructured Bilingual Clinical Notes With Multilingual TransformersIEEE Access10.1109/ACCESS.2023.324552311(16066-16075)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3245523
Guo YGe YYang YAl-Garadi MSarker A(2022)Comparison of Pretraining Models and Strategies for Health-Related Social Media Text ClassificationHealthcare10.3390/healthcare1008147810:8(1478)Online publication date: 5-Aug-2022
https://doi.org/10.3390/healthcare10081478
Show More Cited By

Index Terms

Challenges in understanding clinical notes: why NLP engines fall short and where background knowledge can help
1. Applied computing
  1. Document management and text processing

Recommendations

Detecting negation of medical problems in French clinical notes
IHI '12: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium

When automatically mining narrative clinical notes to extract meaningful information, such as medical problems, it is essential to take into account the context of this information. For instance, determining whether medical conditions are negated or not ...
Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care
Graphical abstract

Display Omitted
Highlights
- Early identification of HHC patients at risk can prevent hospitalizations/ED visits.
Abstract Background/Objective
Between 10 and 25% patients are hospitalized or visit emergency department (ED) during home healthcare (HHC). Given that up to 40% of these negative clinical outcomes are preventable, early and accurate ...
Hospital Readmission Prediction Using Clinical Admission Notes
ACSW '22: Proceedings of the 2022 Australasian Computer Science Week

Clinical notes contain contextualised information beyond structured data relating to patients’ past and current health conditions. Despite the richness, their unstructured, long, and high dimensional nature presents challenges to traditional text ...

Comments

Information & Contributors

Information

Published In

DARE '13: Proceedings of the 2013 international workshop on Data management & analytics for healthcare

November 2013

34 pages

ISBN:9781450324250

DOI:10.1145/2512410

General Chairs:
Ullas Nambiar
EMC India COE, India
,
Niranjan Thirumale
EMC India COE, India

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 ACM 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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CIKM'13

Sponsor:

CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

November 1, 2013

California, San Francisco, USA

Acceptance Rates

DARE '13 Paper Acceptance Rate 5 of 7 submissions, 71%;

Overall Acceptance Rate 5 of 7 submissions, 71%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

17
Total Citations
View Citations
306
Total Downloads

Downloads (Last 12 months)35
Downloads (Last 6 weeks)5

Reflects downloads up to 30 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Trajanov DTrajkovski VDimitrieva MDobreva JJovanovik MKlemen MŽagar ARobnik-Šikonja M(2023)Review of Natural Language Processing in PharmacologyPharmacological Reviews10.1124/pharmrev.122.00071575:4(714-738)Online publication date: 17-Mar-2023
https://doi.org/10.1124/pharmrev.122.000715
Kim HPark YPark YChoi EKim SYou HBae Y(2023)Identifying Alcohol-Related Information From Unstructured Bilingual Clinical Notes With Multilingual TransformersIEEE Access10.1109/ACCESS.2023.324552311(16066-16075)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3245523
Guo YGe YYang YAl-Garadi MSarker A(2022)Comparison of Pretraining Models and Strategies for Health-Related Social Media Text ClassificationHealthcare10.3390/healthcare1008147810:8(1478)Online publication date: 5-Aug-2022
https://doi.org/10.3390/healthcare10081478
Mahmoudi EWu WNajarian CAikens JBynum JVydiswaran V(2022)Identifying Caregiver Availability Using Medical Notes With Rule-Based Natural Language Processing: Retrospective Cohort StudyJMIR Aging10.2196/402415:3(e40241)Online publication date: 22-Sep-2022
https://doi.org/10.2196/40241
Du ZLiu XXu JNan GWang G(2022)Semantic Reasoning with NLI for Assertion Detection in Medical Text2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM55620.2022.9995166(744-748)Online publication date: 6-Dec-2022
https://doi.org/10.1109/BIBM55620.2022.9995166
Wang QLiu YGu JFu H(2021)Medical Entity Recognition Based on BiLSTM with Knowledge Graph and Attention Mechanism2021 4th International Conference on Intelligent Autonomous Systems (ICoIAS)10.1109/ICoIAS53694.2021.00035(149-157)Online publication date: May-2021
https://doi.org/10.1109/ICoIAS53694.2021.00035
Bacchi SGluck SKoblar SJannes JKleinig T(2021)Improving the accuracy of stroke clinical coding with open-source software and natural language processingJournal of Clinical Neuroscience10.1016/j.jocn.2021.10.02494(233-236)Online publication date: Dec-2021
https://doi.org/10.1016/j.jocn.2021.10.024
Marcu GDey AKiesler S(2021)Tensions in Representing Behavioral Data in an Electronic Health RecordComputer Supported Cooperative Work (CSCW)10.1007/s10606-021-09402-7Online publication date: 22-Jun-2021
https://doi.org/10.1007/s10606-021-09402-7
Li ZLian YMa XZhang XLi C(2020)Bio-semantic relation extraction with attention-based external knowledge reinforcementBMC Bioinformatics10.1186/s12859-020-3540-821:1Online publication date: 24-May-2020
https://doi.org/10.1186/s12859-020-3540-8
Motlagh F(2020)DsOn: Ontology-Driven Model for Symptom and Drug Knowledge Extraction on Social Media2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM)10.1109/ICCAKM46823.2020.9051527(552-559)Online publication date: Jan-2020
https://doi.org/10.1109/ICCAKM46823.2020.9051527
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Detecting negation of medical problems in French clinical notes

Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care

Hospital Readmission Prediction Using Clinical Admission Notes