Literature DB >> 31438212

Machine Learning Approaches for Extracting Stage from Pathology Reports in Prostate Cancer.

Raphael Lenain1, Martin G Seneviratne1, Selen Bozkurt1,2, Douglas W Blayney3, James D Brooks4, Tina Hernandez-Boussard1,2.   

Abstract

Clinical and pathological stage are defining parameters in oncology, which direct a patient's treatment options and prognosis. Pathology reports contain a wealth of staging information that is not stored in structured form in most electronic health records (EHRs). Therefore, we evaluated three supervised machine learning methods (Support Vector Machine, Decision Trees, Gradient Boosting) to classify free-text pathology reports for prostate cancer into T, N and M stage groups.

Entities:  

Keywords:  Natural Language Processing; Neoplasm Staging; Prostate Cancer

Mesh：

Year:  2019        PMID： 31438212      PMCID： PMC6712988          DOI： 10.3233/SHTI190515

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN： 0926-9630


  10 in total

1.  Cancer Staging in Electronic Health Records: Strategies to Improve Documentation of These Critical Data.

Authors:  Tracey L Evans; Peter E Gabriel; Lawrence N Shulman
Journal:  J Oncol Pract       Date:  2016-02       Impact factor: 3.840

2.  Cancer statistics, 2018.

Authors:  Rebecca L Siegel; Kimberly D Miller; Ahmedin Jemal
Journal:  CA Cancer J Clin       Date:  2018-01-04       Impact factor: 508.702

3.  Multi-class classification of cancer stages from free-text histology reports using support vector machines.

Authors:  Anthony Nguyen; Darren Moore; Iain McCowan; Mary-Jane Courage
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2007

4.  Symbolic rule-based classification of lung cancer stages from free-text pathology reports.

Authors:  Anthony N Nguyen; Michael J Lawley; David P Hansen; Rayleen V Bowman; Belinda E Clarke; Edwina E Duhig; Shoni Colquist
Journal:  J Am Med Inform Assoc       Date:  2010 Jul-Aug       Impact factor: 4.497

5.  Cancer statistics, 2015.

Authors:  Rebecca L Siegel; Kimberly D Miller; Ahmedin Jemal
Journal:  CA Cancer J Clin       Date:  2015-01-05       Impact factor: 508.702

6.  Prostate cancer - major changes in the American Joint Committee on Cancer eighth edition cancer staging manual.

Authors:  Mark K Buyyounouski; Peter L Choyke; Jesse K McKenney; Oliver Sartor; Howard M Sandler; Mahul B Amin; Michael W Kattan; Daniel W Lin
Journal:  CA Cancer J Clin       Date:  2017-02-21       Impact factor: 508.702

7.  Collection of cancer stage data by classifying free-text medical reports.

Authors:  Iain A McCowan; Darren C Moore; Anthony N Nguyen; Rayleen V Bowman; Belinda E Clarke; Edwina E Duhig; Mary-Jane Fry
Journal:  J Am Med Inform Assoc       Date:  2007-08-21       Impact factor: 4.497

8.  ReCAP: Feasibility and Accuracy of Extracting Cancer Stage Information From Narrative Electronic Health Record Data.

Authors:  Jeremy L Warner; Mia A Levy; Michael N Neuss; Jeremy L Warner; Mia A Levy; Michael N Neuss
Journal:  J Oncol Pract       Date:  2015-08-25       Impact factor: 3.840

9.  Classification of cancer stage from free-text histology reports.

Authors:  Ian McCowan; Darren Moore; Mary-Jane Fry
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2006

10.  Architecture and Implementation of a Clinical Research Data Warehouse for Prostate Cancer.

Authors:  Martin G Seneviratne; Tina Seto; Douglas W Blayney; James D Brooks; Tina Hernandez-Boussard
Journal:  EGEMS (Wash DC)       Date:  2018-06-01
  10 in total
  1 in total

Review 1.  Research and Application of Artificial Intelligence Based on Electronic Health Records of Patients With Cancer: Systematic Review.

Authors:  Xinyu Yang; Dongmei Mu; Hao Peng; Hua Li; Ying Wang; Ping Wang; Yue Wang; Siqi Han
Journal:  JMIR Med Inform       Date:  2022-04-20
  1 in total

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