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

Natural Language Processing (650) Electronic Health Records (971)

Published on in Vol 7, No 4 (2019): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12575, first published .
Extracting Clinical Features From Dictated Ambulatory Consult Notes Using a Commercially Available Natural Language Processing Tool: Pilot, Retrospective, Cross-Sectional Validation Study

Extracting Clinical Features From Dictated Ambulatory Consult Notes Using a Commercially Available Natural Language Processing Tool: Pilot, Retrospective, Cross-Sectional Validation Study

Extracting Clinical Features From Dictated Ambulatory Consult Notes Using a Commercially Available Natural Language Processing Tool: Pilot, Retrospective, Cross-Sectional Validation Study

Authors of this article:

Jeremy Petch1, 2 Author Orcid Image ;   Jane Batt3, 4, 5 Author Orcid Image ;   Joshua Murray6, 7 Author Orcid Image ;   Muhammad Mamdani1, 6, 8, 9 Author Orcid Image
Article Authors Cited by (7) Tweetations (6) Metrics

Journals

  1. Sai Prashanthi G, Deva A, Vadapalli R, Das A. Automated Categorization of Systemic Disease and Duration From Electronic Medical Record System Data Using Finite-State Machine Modeling: Prospective Validation Study. JMIR Formative Research 2020;4(12):e24490 View
  2. McKenzie J, Rajapakshe R, Shen H, Rajapakshe S, Lin A. A Semiautomated Chart Review for Assessing the Development of Radiation Pneumonitis Using Natural Language Processing: Diagnostic Accuracy and Feasibility Study. JMIR Medical Informatics 2021;9(11):e29241 View
  3. Barnes E, Peacock J, Bachmann L. International Classification of Diseases (ICD) Codes Fail to Accurately Identify Injection Drug Use Associated Endocarditis Cases. Journal of Addiction Medicine 2022;16(1):27 View
  4. Gauthier M, Law J, Le L, Li J, Zahir S, Nirmalakumar S, Sung M, Pettengell C, Aviv S, Chu R, Sacher A, Liu G, Bradbury P, Shepherd F, Leighl N. Automating Access to Real-World Evidence. JTO Clinical and Research Reports 2022;3(6):100340 View
  5. Petch J, Kempainnen J, Pettengell C, Aviv S, Butler B, Pond G, Saha A, Bogach J, Allard-Coutu A, Sztur P, Ranisau J, Levine M. Developing a Data and Analytics Platform to Enable a Breast Cancer Learning Health System at a Regional Cancer Center. JCO Clinical Cancer Informatics 2023;(7) View
  6. Moulson R, Law J, Sacher A, Liu G, Shepherd F, Bradbury P, Eng L, Iczkovitz S, Abbie E, Elia-Pacitti J, Ewara E, Mokriak V, Weiss J, Pettengell C, Leighl N. Real-World Outcomes of Patients with Advanced Epidermal Growth Factor Receptor-Mutated Non-Small Cell Lung Cancer in Canada Using Data Extracted by Large Language Model-Based Artificial Intelligence. Current Oncology 2024;31(4):1947 View
  7. Moulson R, Feugère G, Moreira-Lucas T, Dequen F, Weiss J, Smith J, Brezden-Masley C. Real-World Treatment Patterns and Clinical Outcomes among Patients Receiving CDK4/6 Inhibitors for Metastatic Breast Cancer in a Canadian Setting Using AI-Extracted Data. Current Oncology 2024;31(4):2172 View