Literature DB >> 20652738

Pattern-based information extraction from pathology reports for cancer registration.

Giulio Napolitano1, Colin Fox, Richard Middleton, David Connolly.   

Abstract

OBJECTIVE: To evaluate precision and recall rates for the automatic extraction of information from free-text pathology reports. To assess the impact that implementation of pattern-based methods would have on cancer registration completeness.
METHOD: Over 300,000 electronic pathology reports were scanned for the extraction of Gleason score, Clark level and Breslow depth, by a number of Perl routines progressively enhanced by a trial-and-error method. An additional test set of 915 reports potentially containing Gleason score was used for evaluation.
RESULTS: Values for recall and precision of over 98 and 99%, respectively, were easily reached. Potential increase in cancer staging completeness of up to 32% was proved.
CONCLUSIONS: In cancer registration, simple pattern matching applied to free-text documents can be effectively used to improve completeness and accuracy of pathology information.

Entities:  

Mesh:

Year:  2010        PMID: 20652738     DOI: 10.1007/s10552-010-9616-4

Source DB:  PubMed          Journal:  Cancer Causes Control        ISSN: 0957-5243            Impact factor:   2.506


  10 in total

1.  A Frame-Based NLP System for Cancer-Related Information Extraction.

Authors:  Yuqi Si; Kirk Roberts
Journal:  AMIA Annu Symp Proc       Date:  2018-12-05

2.  Efficient identification of nationally mandated reportable cancer cases using natural language processing and machine learning.

Authors:  John D Osborne; Matthew Wyatt; Andrew O Westfall; James Willig; Steven Bethard; Geoff Gordon
Journal:  J Am Med Inform Assoc       Date:  2016-03-28       Impact factor: 4.497

3.  Automated extraction of Biomarker information from pathology reports.

Authors:  Jeongeun Lee; Hyun-Je Song; Eunsil Yoon; Seong-Bae Park; Sung-Hye Park; Jeong-Wook Seo; Peom Park; Jinwook Choi
Journal:  BMC Med Inform Decis Mak       Date:  2018-05-21       Impact factor: 2.796

4.  Artificial Intelligence-Driven Structurization of Diagnostic Information in Free-Text Pathology Reports.

Authors:  Pericles S Giannaris; Zainab Al-Taie; Mikhail Kovalenko; Nattapon Thanintorn; Olha Kholod; Yulia Innokenteva; Emily Coberly; Shellaine Frazier; Katsiarina Laziuk; Mihail Popescu; Chi-Ren Shyu; Dong Xu; Richard D Hammer; Dmitriy Shin
Journal:  J Pathol Inform       Date:  2020-02-11

5.  Natural language processing systems for pathology parsing in limited data environments with uncertainty estimation.

Authors:  Anobel Y Odisho; Briton Park; Nicholas Altieri; John DeNero; Matthew R Cooperberg; Peter R Carroll; Bin Yu
Journal:  JAMIA Open       Date:  2020-10-14

6.  Using text mining techniques to extract prostate cancer predictive information (Gleason score) from semi-structured narrative laboratory reports in the Gauteng province, South Africa.

Authors:  Naseem Cassim; Michael Mapundu; Victor Olago; Turgay Celik; Jaya Anna George; Deborah Kim Glencross
Journal:  BMC Med Inform Decis Mak       Date:  2021-11-25       Impact factor: 2.796

7.  Improving natural language information extraction from cancer pathology reports using transfer learning and zero-shot string similarity.

Authors:  Briton Park; Nicholas Altieri; John DeNero; Anobel Y Odisho; Bin Yu
Journal:  JAMIA Open       Date:  2021-09-30

8.  A Question-and-Answer System to Extract Data From Free-Text Oncological Pathology Reports (CancerBERT Network): Development Study.

Authors:  Joseph Ross Mitchell; Phillip Szepietowski; Rachel Howard; Phillip Reisman; Jennie D Jones; Patricia Lewis; Brooke L Fridley; Dana E Rollison
Journal:  J Med Internet Res       Date:  2022-03-23       Impact factor: 7.076

9.  Rule-Based Information Extraction from Free-Text Pathology Reports Reveals Trends in South African Female Breast Cancer Molecular Subtypes and Ki67 Expression.

Authors:  Okechinyere J Achilonu; Elvira Singh; Gideon Nimako; René M J C Eijkemans; Eustasius Musenge
Journal:  Biomed Res Int       Date:  2022-01-20       Impact factor: 3.411

Review 10.  Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing.

Authors:  Liwei Wang; Sunyang Fu; Andrew Wen; Xiaoyang Ruan; Huan He; Sijia Liu; Sungrim Moon; Michelle Mai; Irbaz B Riaz; Nan Wang; Ping Yang; Hua Xu; Jeremy L Warner; Hongfang Liu
Journal:  JCO Clin Cancer Inform       Date:  2022-07
  10 in total

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