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

Machine Learning (1501) Electronic Health Records (1068) Ontologies, Classifications, and Coding (243)

Published on in Vol 5, No 2 (2017): Apr-Jun

Effective Information Extraction Framework for Heterogeneous Clinical Reports Using Online Machine Learning and Controlled Vocabularies

Effective Information Extraction Framework for Heterogeneous Clinical Reports Using Online Machine Learning and Controlled Vocabularies

Effective Information Extraction Framework for Heterogeneous Clinical Reports Using Online Machine Learning and Controlled Vocabularies

Authors of this article:

Shuai Zheng1 Author Orcid Image ;   James J Lu2 Author Orcid Image ;   Nima Ghasemzadeh3 Author Orcid Image ;   Salim S Hayek3 Author Orcid Image ;   Arshed A Quyyumi3 Author Orcid Image ;   Fusheng Wang4 Author Orcid Image
Article Authors Cited by (18) Tweetations (25) Metrics

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  9. Bacco L, Russo F, Ambrosio L, D’Antoni F, Vollero L, Vadalà G, Dell’Orletta F, Merone M, Papalia R, Denaro V. Natural language processing in low back pain and spine diseases: A systematic review. Frontiers in Surgery 2022;9 View
  10. Saber I, Adamski A, Kuchibhatla M, Abe K, Beckman M, Reyes N, Schulteis R, Pendurthi Singh B, Sitlinger A, Thames E, Ortel T. Racial differences in venous thromboembolism: A surveillance program in Durham County, North Carolina. Research and Practice in Thrombosis and Haemostasis 2022;6(5):e12769 View
  11. Wendelboe A, Saber I, Dvorak J, Adamski A, Feland N, Reyes N, Abe K, Ortel T, Raskob G. Exploring the Applicability of Using Natural Language Processing to Support Nationwide Venous Thromboembolism Surveillance: Model Evaluation Study. JMIR Bioinformatics and Biotechnology 2022;3(1):e36877 View
  12. Falezza F, Piccinelli N, De Rossi G, Roberti A, Kronreif G, Setti F, Fiorini P, Muradore R. Modeling of Surgical Procedures Using Statecharts for Semi-Autonomous Robotic Surgery. IEEE Transactions on Medical Robotics and Bionics 2021;3(4):888 View

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