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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

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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.

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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
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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

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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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2013

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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%

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Cited By

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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
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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
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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
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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
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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
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