Post-Structuring Radiology Reports of Breast Cancer Patients for Clinical Quality Assurance

Shreyasi Pathak, Jorit van Rossen, Onno Vijlbrief, Jeroen Geerdink, Christin Seifert, Maurice van Keulen

    Research output: Contribution to journalArticleAcademicpeer-review

    4 Citations (Scopus)
    172 Downloads (Pure)

    Abstract

    Hospitals often set protocols based on well defined standards to maintain the quality of patient reports. To ensure that the clinicians conform to the protocols, quality assurance of these reports is needed. Patient reports are currently written in free-text format, which complicates the task of quality assurance. In this paper, we present a machine learning based natural language processing system for automatic quality assurance of radiology reports on breast cancer. This is achieved in three steps: we i) identify the top-level structure (headings) of the report, ii) classify the report content into the top-level headings, iii) convert the free-text detailed findings in the report to a semi-structured format (post-structuring). Top level structure and content of report were predicted with an F1 score of 0.97 and 0.94, respectively using Support Vector Machine (SVM) classifiers. For automatic structuring, our proposed hierarchical Conditional Random Field (CRF) outperformed the baseline CRF with an F1 score of 0.78 vs 0.71. The determined structure of the report is represented in semi-structured XML format of the free-text report, which helps to easily visualize the conformance of the findings to the protocols. This format also allows easy extraction of specific information for other purposes such as search, evaluation and research.
    Original languageEnglish
    Article number8705380
    Pages (from-to)1883-1894
    Number of pages12
    JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
    Volume17
    Issue number6
    Early online date3 May 2019
    DOIs
    Publication statusPublished - Nov 2020

    Fingerprint

    Dive into the research topics of 'Post-Structuring Radiology Reports of Breast Cancer Patients for Clinical Quality Assurance'. Together they form a unique fingerprint.
    • Comparing Process Models for Patient Populations: Application in Breast Cancer Care

      Marazza, F., Bukhsh, F. A., Vijlbrief, O., Geerdink, J., Pathak, S., van Keulen, M. & Seifert, C., 1 Jan 2019, Business Process Management Workshops - BPM 2019 International Workshops, Revised Selected Papers. Di Francescomarino, C., Dijkman, R. & Zdun, U. (eds.). Cham: Springer, p. 496-507 12 p. (Lecture Notes in Business Information Processing; vol. 362).

      Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

      Open Access
      File
      6 Citations (Scopus)
      1 Downloads (Pure)

    Cite this