This paper is in the following e-collection/theme issue:

Theme Issue 2021: Health Natural Language Processing and Applications (16) Natural Language Processing (706) Decision Support for Health Professionals (1210) Electronic Health Records (1068) Clinical Information and Decision Making (1434) Machine Learning (1501) Computer-Aided Diagnosis (135) Secondary Use of Clinical Data for Research and Surveillance (375)

Published on in Vol 9, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29241, first published .
A Semiautomated Chart Review for Assessing the Development of Radiation Pneumonitis Using Natural Language Processing: Diagnostic Accuracy and Feasibility Study

A Semiautomated Chart Review for Assessing the Development of Radiation Pneumonitis Using Natural Language Processing: Diagnostic Accuracy and Feasibility Study

A Semiautomated Chart Review for Assessing the Development of Radiation Pneumonitis Using Natural Language Processing: Diagnostic Accuracy and Feasibility Study

Authors of this article:

Jordan McKenzie1 Author Orcid Image ;   Rasika Rajapakshe2, 3 Author Orcid Image ;   Hua Shen4 Author Orcid Image ;   Shan Rajapakshe5 Author Orcid Image ;   Angela Lin3, 6 Author Orcid Image
Article Authors Cited by (5) Tweetations (3) Metrics

Jordan McKenzie   1 , BSc ;   Rasika Rajapakshe   2, 3 , PhD ;   Hua Shen   4 , MMath, PhD ;   Shan Rajapakshe   5 , BSc, MSc ;   Angela Lin   3, 6 , MD

1 Northern Medical Program, Faculty of Medicine, University of British Columbia, Prince George, BC, Canada

2 Medical Physics, BC Cancer, Kelowna, BC, Canada

3 Department of Surgery, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada

4 Department of Mathematics and Statistics, University of Calgary, Calgary, AB, Canada

5 Island Medical Program, Faculty of Medicine, University of British Columbia, Victoria, BC, Canada

6 Radiation Oncology, BC Cancer, Kelowna, BC, Canada

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