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

Clinical Information and Decision Making (1915) Decision Support for Health Professionals (1539) Natural Language Processing (917) Machine Learning (2076)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43153, first published .
Natural Language Processing in a Clinical Decision Support System for the Identification of Venous Thromboembolism: Algorithm Development and Validation

Natural Language Processing in a Clinical Decision Support System for the Identification of Venous Thromboembolism: Algorithm Development and Validation

Natural Language Processing in a Clinical Decision Support System for the Identification of Venous Thromboembolism: Algorithm Development and Validation

Authors of this article:

Zhi-Geng Jin1 Author Orcid Image ;   Hui Zhang1 Author Orcid Image ;   Mei-Hui Tai2 Author Orcid Image ;   Ying Yang3 Author Orcid Image ;   Yuan Yao4 Author Orcid Image ;   Yu-Tao Guo1 Author Orcid Image
Article Authors Cited by (11) Tweetations (4) Metrics

Journals

  1. Reyes Gil M, Pantanowitz J, Rashidi H. Venous thromboembolism in the era of machine learning and artificial intelligence in medicine. Thrombosis Research 2024;242:109121 View
  2. Li Y, Zhang L, Liu H, Li Y, Liu Z. Research progress of artificial intelligence and machine learning in pulmonary embolism. Frontiers in Medicine 2025;12 View
  3. Naser A, Vyas R, Morgan A, Kalaiger A, Kharawala A, Nagraj S, Agarwal R, Maliha M, Mangeshkar S, Singh N, Satish V, Mathai S, Palaiodimos L, Faillace R. Role of Artificial Intelligence in the Diagnosis and Management of Pulmonary Embolism: A Comprehensive Review. Diagnostics 2025;15(7):889 View
  4. Song M, Liu D. International visualization analysis of research hotspots and development trends in the study of clinical decision support systems utilizing CiteSpace. Frontiers in Medicine 2025;12 View
  5. Jin Z, Zhang Z, Liu B, Wang H, Yang Y, Ren L, Zhang H, Ji W, Zhai Z, Guo Y. Smart Technology Facilitated Patient-Centered Venous Thromboembolism Management (the SmaVTE Study): Protocol for a Randomized Controlled Trial. JMIR Research Protocols 2025;14:e67254 View
  6. Novoa-Laurentiev J, Bowen M, Pullman A, Song W, Syrowatka A, Chen J, Sainlaire M, Chang F, Gray K, Panta P, Liu L, Nawab K, Hijjawi S, Schreiber R, Zhou L, Dykes P. An Extraction Tool for Venous Thromboembolism Symptom Identification in Primary Care Notes to Facilitate Electronic Clinical Quality Measure Reporting: Algorithm Development and Validation Study. JMIR Medical Informatics 2025;13:e63720 View
  7. Cellina M, Pavan M, Finardi N, Cicchetti F, Cè M, Biondetti P, Lanza C, Carriero S, Carrafiello G. Advancements in Acute Pulmonary Embolism Diagnosis and Treatment: A Narrative Review of Emerging Imaging Techniques and Intravascular Interventions. Journal of Cardiovascular Development and Disease 2025;12(9):333 View
  8. Gu Y, Yang Y, Gao X, Wang Y, Yang L, Wei Y. Application of artificial intelligence in risk assessment and management of venous thromboembolism: scoping review. Frontiers in Physiology 2025;16 View
  9. Jafarpour H, Wu G, Cheligeer C, Yan J, Xu Y, Southern D, Eastwood C, Zeng Y, Quan H. Preprocessing narrative texts in electronic medical records to identify hospital adverse events: A scoping review. Artificial Intelligence in Medicine 2025;170:103281 View
  10. Alnor A, Højer Adelhelm J, Pedersen L, Faurø K, Laursen M, Lynggaard R, Vinholt P. Early clinical implementation and evaluation of an NLP-Based AI system for thrombophilia assessment using electronic health records. International Journal of Medical Informatics 2026;206:106135 View

Books/Policy Documents

  1. Laabidi A, Aissaoui M, Madani M. Proceedings of Ninth International Congress on Information and Communication Technology. View