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Breast Cancer Research and Treatment

, Volume 161, Issue 2, pp 203–211 | Cite as

Using machine learning to parse breast pathology reports

  • Adam Yala
  • Regina Barzilay
  • Laura Salama
  • Molly GriffinEmail author
  • Grace Sollender
  • Aditya Bardia
  • Constance Lehman
  • Julliette M. Buckley
  • Suzanne B. Coopey
  • Fernanda Polubriaginof
  • Judy E. Garber
  • Barbara L. Smith
  • Michele A. Gadd
  • Michelle C. Specht
  • Thomas M. Gudewicz
  • Anthony J. Guidi
  • Alphonse Taghian
  • Kevin S. Hughes
Preclinical Study

Abstract

Purpose

Extracting information from electronic medical record is a time-consuming and expensive process when done manually. Rule-based and machine learning techniques are two approaches to solving this problem. In this study, we trained a machine learning model on pathology reports to extract pertinent tumor characteristics, which enabled us to create a large database of attribute searchable pathology reports. This database can be used to identify cohorts of patients with characteristics of interest.

Methods

We collected a total of 91,505 breast pathology reports from three Partners hospitals: Massachusetts General Hospital, Brigham and Women’s Hospital, and Newton-Wellesley Hospital, covering the period from 1978 to 2016. We trained our system with annotations from two datasets, consisting of 6295 and 10,841 manually annotated reports. The system extracts 20 separate categories of information, including atypia types and various tumor characteristics such as receptors. We also report a learning curve analysis to show how much annotation our model needs to perform reasonably.

Results

The model accuracy was tested on 500 reports that did not overlap with the training set. The model achieved accuracy of 90% for correctly parsing all carcinoma and atypia categories for a given patient. The average accuracy for individual categories was 97%. Using this classifier, we created a database of 91,505 parsed pathology reports.

Conclusions

Our learning curve analysis shows that the model can achieve reasonable results even when trained on a few annotations. We developed a user-friendly interface to the database that allows physicians to easily identify patients with target characteristics and export the matching cohort. This model has the potential to reduce the effort required for analyzing large amounts of data from medical records, and to minimize the cost and time required to glean scientific insight from these data.

Keywords

Machine learning Pathology reports Natural language processing Breast pathology Atypia Hyperplasia Carcinoma in situ 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Adam Yala
    • 1
  • Regina Barzilay
    • 1
  • Laura Salama
    • 3
  • Molly Griffin
    • 2
    Email author
  • Grace Sollender
    • 8
  • Aditya Bardia
    • 10
  • Constance Lehman
    • 5
  • Julliette M. Buckley
    • 2
  • Suzanne B. Coopey
    • 2
  • Fernanda Polubriaginof
    • 9
  • Judy E. Garber
    • 6
  • Barbara L. Smith
    • 2
  • Michele A. Gadd
    • 2
  • Michelle C. Specht
    • 2
  • Thomas M. Gudewicz
    • 4
  • Anthony J. Guidi
    • 7
  • Alphonse Taghian
    • 3
  • Kevin S. Hughes
    • 2
  1. 1.Department of Electrical Engineering and Computer Science, CSAILMITCambridgeUSA
  2. 2.Division of Surgical OncologyMGHBostonUSA
  3. 3.Department of Radiation OncologyMGHBostonUSA
  4. 4.Department of PathologyMGHBostonUSA
  5. 5.Department of RadiologyMGHBostonUSA
  6. 6.Department of Medical OncologyDFCIBostonUSA
  7. 7.Department of PathologyNWHNewtonUSA
  8. 8.Geisel School of Medicine at DartmouthHanoverUSA
  9. 9.Department of Biomedical InformaticsColumbia UniversityNew YorkUSA
  10. 10.Department of Medical OncologyMGHBostonUSA

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