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

Journals

  1. Spasic I, Nenadic G. Clinical Text Data in Machine Learning: Systematic Review. JMIR Medical Informatics 2020;8(3):e17984 View
  2. Zheng S, Jabbour S, O'Reilly S, Lu J, Dong L, Ding L, Xiao Y, Yue N, Wang F, Zou W. Automated Information Extraction on Treatment and Prognosis for Non–Small Cell Lung Cancer Radiotherapy Patients: Clinical Study. JMIR Medical Informatics 2018;6(1):e8 View
  3. Hardjojo A, Gunachandran A, Pang L, Abdullah M, Wah W, Chong J, Goh E, Teo S, Lim G, Lee M, Hsu W, Lee V, Chen M, Wong F, Phang J. Validation of a Natural Language Processing Algorithm for Detecting Infectious Disease Symptoms in Primary Care Electronic Medical Records in Singapore. JMIR Medical Informatics 2018;6(2):e36 View
  4. Zhou L, Suominen H, Gedeon T. Adapting State-of-the-Art Deep Language Models to Clinical Information Extraction Systems: Potentials, Challenges, and Solutions. JMIR Medical Informatics 2019;7(2):e11499 View
  5. Dantes R, Zheng S, Lu J, Beckman M, Krishnaswamy A, Richardson L, Chernetsky-Tejedor S, Wang F. Improved Identification of Venous Thromboembolism From Electronic Medical Records Using a Novel Information Extraction Software Platform. Medical Care 2018;56(9):e54 View
  6. Raghav R, Dhavachelvan P, Vijayakumar V, Subramaniyaswamy V, Abawajy J, Yang L. Bigdata fog based cyber physical system for classifying, identifying and prevention of SARS disease. Journal of Intelligent & Fuzzy Systems 2019;36(5):4361 View
  7. Richter-Pechanski P, Geis N, Kiriakou C, Schwab D, Dieterich C. Automatic extraction of 12 cardiovascular concepts from German discharge letters using pre-trained language models. DIGITAL HEALTH 2021;7:205520762110576 View
  8. Biran O, Feder O, Moatti Y, Kiourtis A, Kyriazis D, Manias G, Mavrogiorgou A, Sgouros N, T. Barata M, Oldani I, Sanguino M, Kranas P, Baroni S. PolicyCLOUD: A prototype of a cloud serverless ecosystem for policy analytics. Data & Policy 2022;4 View
  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

Books/Policy Documents

  1. Gong L. Artificial Intelligence - Emerging Trends and Applications. View
  2. Catania L. Foundations of Artificial Intelligence in Healthcare and Bioscience. View
  3. Manias G, Mavrogiorgou A, Kiourtis A, Kyriazis D. Artificial Intelligence Applications and Innovations. View
  4. Fu S, Wen A, Liu H. Clinical Research Informatics. View