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

Journals

  1. Miller T, Li Y, Masino A, Vallee E, Burrows E, Ramos M, Alonzo T, Gerbing R, Castellino S, Hawkins D, Lash T, Aplenc R, Grundmeier R. Automated Ascertainment of Typhlitis From the Electronic Health Record. JCO Clinical Cancer Informatics 2022;(6) View
  2. Ostropolets A, Hripcsak G, Husain S, Richter L, Spotnitz M, Elhussein A, Ryan P. Scalable and interpretable alternative to chart review for phenotype evaluation using standardized structured data from electronic health records. Journal of the American Medical Informatics Association 2023;31(1):119 View
  3. Yonamine S, Ma C, Alabi R, Kaidonis G, Chan L, Borkar D, Stein J, Arnold B, Sun C. Comparison of Diagnosis Codes to Clinical Notes in Classifying Patients with Diabetic Retinopathy. Ophthalmology Science 2024;4(6):100564 View
  4. Nishiyama T, Yamaguchi A, Han P, Pereira L, Otsuki Y, Andrade G, Kudo N, Yada S, Wakamiya S, Aramaki E, Takada M, Toi M. Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study. JMIR Medical Informatics 2024;12:e58977 View
  5. Joo S, Yang S, Lee S, Park S, Park T, Rhee S, Cha J, Rhie S, Hwang H, Kim Y, Chung E. Trends in Antidiabetic Drug Use and Safety of Metformin in Diabetic Patients with Varying Degrees of Chronic Kidney Disease from 2010 to 2021 in Korea: Retrospective Cohort Study Using the Common Data Model. Pharmaceuticals 2024;17(10):1369 View