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

AI applications in genomics and pathology informatics, including ‘Generative AI’ (11) Natural Language Processing (793) Machine Learning (1715) Computer-Aided Diagnosis (180) Clinical Information and Decision Making (1633) Decision Support for Health Professionals (1331) Electronic Health Records (1169) Surveillance Systems (490) Secondary Use of Clinical Data for Research and Surveillance (413)

Published on in Vol 3, No 1 (2022): Jan-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36877, first published .
Exploring the Applicability of Using Natural Language Processing to Support Nationwide Venous Thromboembolism Surveillance: Model Evaluation Study

Exploring the Applicability of Using Natural Language Processing to Support Nationwide Venous Thromboembolism Surveillance: Model Evaluation Study

Exploring the Applicability of Using Natural Language Processing to Support Nationwide Venous Thromboembolism Surveillance: Model Evaluation Study

Authors of this article:

Aaron Wendelboe1 Author Orcid Image ;   Ibrahim Saber2 Author Orcid Image ;   Justin Dvorak1 Author Orcid Image ;   Alys Adamski3 Author Orcid Image ;   Natalie Feland1 Author Orcid Image ;   Nimia Reyes3 Author Orcid Image ;   Karon Abe3 Author Orcid Image ;   Thomas Ortel2 Author Orcid Image ;   Gary Raskob1 Author Orcid Image
Article Authors Cited by (4) Tweetations (3) Metrics

Journals

  1. Jin Z, Zhang H, Tai M, Yang Y, Yao Y, Guo Y. Natural Language Processing in a Clinical Decision Support System for the Identification of Venous Thromboembolism: Algorithm Development and Validation. Journal of Medical Internet Research 2023;25:e43153 View
  2. Wendelboe A, Weitz J. Global Health Burden of Venous Thromboembolism. Arteriosclerosis, Thrombosis, & Vascular Biology 2024;44(5):1007 View
  3. Lam B, Chrysafi P, Chiasakul T, Khosla H, Karagkouni D, McNichol M, Adamski A, Reyes N, Abe K, Mantha S, Vlachos I, Zwicker J, Patell R. Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis. Blood Advances 2024;8(12):2991 View
  4. Christensen M, Stubblefield W, Wang G, Altheimer A, Ouadah S, Birrenkott D, Peters G, Prucnal C, Harshbarger S, Chang K, Storrow A, Ward M, Collins S, Kabrhel C, Wrenn J. Derivation and external validation of a portable method to identify patients with pulmonary embolism from radiology reports: The READ-PE algorithm. Thrombosis Research 2024;241:109105 View