Using case-level context to classify cancer pathology reports

Shang Gao, Mohammed Alawad, Noah Schaefferkoetter, Lynne Penberthy, Xiao Cheng Wu, Eric B. Durbin, Linda Coyle, Arvind Ramanathan, Georgia Tourassi

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Individual electronic health records (EHRs) and clinical reports are often part of a larger sequence—for example, a single patient may generate multiple reports over the trajectory of a disease. In applications such as cancer pathology reports, it is necessary not only to extract information from individual reports, but also to capture aggregate information regarding the entire cancer case based off case-level context from all reports in the sequence. In this paper, we introduce a simple modular add-on for capturing case-level context that is designed to be compatible with most existing deep learning architectures for text classification on individual reports. We test our approach on a corpus of 431,433 cancer pathology reports, and we show that incorporating case-level context significantly boosts classification accuracy across six classification tasks—site, subsite, laterality, histology, behavior, and grade. We expect that with minimal modifications, our add-on can be applied towards a wide range of other clinical text-based tasks.

Original languageEnglish
Article numbere0232840
JournalPLoS ONE
Volume15
Issue number5
DOIs
StatePublished - May 2020

Bibliographical note

Publisher Copyright:
© 2020 Gao et al. This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

ASJC Scopus subject areas

  • General

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