research-article

Information extraction from pathology reports in a hospital setting

Authors:

David Martinez,

Yue LiAuthors Info & Claims

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

Pages 1877 - 1882

https://doi.org/10.1145/2063576.2063846

Published: 24 October 2011 Publication History

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Abstract

As more health data becomes available, information extraction aims to make an impact on the workflows of hospitals and care centers. One of the targeted areas is the management of pathology reports, which are employed for cancer diagnosis and staging. In this work we integrate text mining tools in the workflow of the Royal Melbourne Hospital, to extract information from pathology reports with minimal expert intervention. Our framework relies on coarse-grained annotation (at document level), making it highly portable. Our evaluation shows that the kind of language used in these reports makes it feasible to extract information with high precision and recall, by means of state-of-the-art classification methods, and feature engineering.

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Cited By

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Yoon HKlasky HBlanchard AChristian JDurbin EWu XStroup ADoherty JCoyle LPenberthy LTourassi G(2024)Development of message passing-based graph convolutional networks for classifying cancer pathology reportsBMC Medical Informatics and Decision Making10.1186/s12911-024-02662-524:S5Online publication date: 17-Sep-2024
https://doi.org/10.1186/s12911-024-02662-5
Sreekrishna MPrem Jacob T(2023)Quantitative feature extraction of unstructured data from GitLab BioAI pathology reports of cancer using an enhanced RPA NLP methodJournal of Intelligent & Fuzzy Systems10.3233/JIFS-23162545:4(5265-5276)Online publication date: 4-Oct-2023
https://doi.org/10.3233/JIFS-231625
Achilonu OOlago VSingh EEijkemans RNimako GMusenge E(2021)A Text Mining Approach in the Classification of Free-Text Cancer Pathology Reports from the South African National Health Laboratory ServicesInformation10.3390/info1211045112:11(451)Online publication date: 30-Oct-2021
https://doi.org/10.3390/info12110451
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Index Terms

Information extraction from pathology reports in a hospital setting
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems

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Information & Contributors

Information

Published In

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

October 2011

2712 pages

ISBN:9781450307178

DOI:10.1145/2063576

Editors:
Bettina Berendt,
Arjen de Vries,
Wenfei Fan,
Craig Macdonald
University of Glasgow, UK
,
Iadh Ounis
University of Glasgow, UK
,
Ian Ruthven
University of Strathclyde, UK

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: 24 October 2011

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CIKM '11

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CIKM '11: International Conference on Information and Knowledge Management

October 24 - 28, 2011

Glasgow, Scotland, UK

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Cited By

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Yoon HKlasky HBlanchard AChristian JDurbin EWu XStroup ADoherty JCoyle LPenberthy LTourassi G(2024)Development of message passing-based graph convolutional networks for classifying cancer pathology reportsBMC Medical Informatics and Decision Making10.1186/s12911-024-02662-524:S5Online publication date: 17-Sep-2024
https://doi.org/10.1186/s12911-024-02662-5
Sreekrishna MPrem Jacob T(2023)Quantitative feature extraction of unstructured data from GitLab BioAI pathology reports of cancer using an enhanced RPA NLP methodJournal of Intelligent & Fuzzy Systems10.3233/JIFS-23162545:4(5265-5276)Online publication date: 4-Oct-2023
https://doi.org/10.3233/JIFS-231625
Achilonu OOlago VSingh EEijkemans RNimako GMusenge E(2021)A Text Mining Approach in the Classification of Free-Text Cancer Pathology Reports from the South African National Health Laboratory ServicesInformation10.3390/info1211045112:11(451)Online publication date: 30-Oct-2021
https://doi.org/10.3390/info12110451
Gaudet-Blavignac CFoufi VBjelogrlic MLovis C(2021)Use of the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) for Processing Free Text in Health Care: Systematic Scoping ReviewJournal of Medical Internet Research10.2196/2459423:1(e24594)Online publication date: 26-Jan-2021
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Dai HYang YWang TLin YLu PWu CChang YLee YZhang YHsu YWu HKe CHuang CWang YYang SHsiao KLiu KChen LChang IChao KLiu T(2021)Cancer Registry Coding via Hybrid Neural Symbolic Systems in the Cross-Hospital SettingIEEE Access10.1109/ACCESS.2021.30991759(112081-112096)Online publication date: 2021
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Deshmukh PPhalnikar R(2021)Prognostic elements extraction from documents to detect prognostic stageComputer Methods in Biomechanics and Biomedical Engineering10.1080/10255842.2021.195535925:4(371-386)Online publication date: 28-Jul-2021
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Giannaris PAl-Taie ZKovalenko MThanintorn NKholod OInnokenteva YCoberly EFrazier SLaziuk KPopescu MShyu CXu DHammer RShin D(2020)Artificial intelligence-driven structurization of diagnostic information in free-text pathology reportsJournal of Pathology Informatics10.4103/jpi.jpi_30_1911:1(4)Online publication date: 2020
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Nguyen AO'Dwyer JVu TWebb PJohnatty SSpurdle A(2020)Generating high-quality data abstractions from scanned clinical records: text-mining-assisted extraction of endometrial carcinoma pathology features as proof of principleBMJ Open10.1136/bmjopen-2020-03774010:6(e037740)Online publication date: 11-Jun-2020
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