R25 SECTION 6 - Natural Language Processing in Radiology

R25 VOICE - Section 6 - Natural Language Processing in Radiology Yin Aphinyanaphongs Papers discussed in this Section 6 Podcast: H. Salehinejad, J. Barfett, S. Valaee, E. Colak, A. Mnatzakanian, and T. Dowdell. Interpretation of mammogram and chest radiograph reports using deep neural networks-preliminary results. arXiv preprint arXiv:1708.09254, 2017. Hassanpour S, Langlotz CP. Predicting High Imaging Utilization Based on Initial Radiology Reports: A Feasibility Study of Machine Learning. Acad Radiol 2016; 23 (01) 84-89. Pons E., Braun L.M.M., Hunink M.G.M. et al. (2016) Natural language processing in radiology: a systematic review. Radiology, 279, 329–343. Trivedi, H., Mesterhazy, J., Laguna, B. et al. Automatic Determination of the Need for Intravenous Contrast in Musculoskeletal MRI Examinations Using IBM Watson’s Natural Language Processing Algorithm. J Digit Imaging (2017). https://doi.org/10.1007/s10278-017-0021-3 Podcast Contents Why These Papers NLP Review Defining NLP NLP Pipeline in Figure 1 Radlex Evaluation Measures - F1 Types Diagnostic Surveillance Cohort Building Query based case retrieval Quality Assessment in radiologic practice Communication of critical results Clinical Support Services Resources in Table 2 Operational Barriers Future Research Needs IV Contrast Why Chosen? Notes Processing Time Discussion Error analysis Cloud Service Passive Workflow integration. Predicting High Imaging Utilization Why Chosen? Notes SVM usage. Document-Feature Matrix Overfit Interpretation of Mammograms Why Chosen? Notes Bi-directional CNN Passive Workflow Integration Preprocessing Why Deep Learning Questions