Joeky Senders

157 Deep Learning and NLP learning curves 18. Gonçalves S, Cortez P, Moro S. A deep learning classifier for sentence classification in biomedical and computer science abstracts. Neural Computing and Applications . Published online 2019. doi:10.1007/ s00521-019-04334-2 19. Cai T, Giannopoulos AA, Yu S, et al. Natural Language Processing Technologies in Radiology Research and Clinical Applications. Radiographics . 2016;36(1):176-191. doi:10.1148/rg.2016150080 20. Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Inform Assoc . 2011;18(5):544-551. doi:10.1136/amiajnl-2011-000464 21. Zhang W, Yoshida T, Tang X. TFIDF, LSI and multi-word in information retrieval and text categorization. In: 2008 IEEE International Conference on Systems, Man and Cybernetics . ; 2008:108-113. doi:10.1109/ ICSMC.2008.4811259 22. Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging . 2018;9(4):611-629. doi:10.1007/s13244-018-0639-9 23. Henderson AR. The bootstrap: a technique for data-driven statistics. Using computer-intensive analyses to explore experimental data. Clin Chim Acta . 2005;359(1-2):1-26. doi:10.1016/j.cccn.2005.04.002 24. Ranstam J, Cook JA. LASSO regression. BJS . 2018;105(10):1348-1348. doi:10.1002/bjs.10895 25. Zola P, Cortez P, Ragno C, Brentari E. Social Media Cross-Source and Cross-Domain Sentiment Classification. International Journal of Information Technology & Decision Making (IJITDM) . 2019;18(05):1469-1499. 26. Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models: a framework for some traditional and novel measures. Epidemiology . 2010;21(1):128-138. doi:10.1097/ EDE.0b013e3181c30fb2 27. Modern Optimization with R | Paulo Cortez | Springer. Accessed April 6, 2020. https://www.springer.com/ gp/book/9783319082622 28. Labovitz DL. Accuracy and yield of ICD-9 codes for identifying children with ischemic stroke. Published online November 22, 2018. Accessed November 22, 2018. http://n.neurology.org/content/accuracy-and- yield-icd-9-codes-identifying-children-ischemic-stroke 29. Pimentel MA, Browne EN, Janardhana PM, et al. Assessment of the Accuracy of Using ICD-9 Codes to Identify Uveitis, Herpes Zoster Ophthalmicus, Scleritis, and Episcleritis. JAMA Ophthalmol . 2016;134(9):1001- 1006. doi:10.1001/jamaophthalmol.2016.2166 30. Guevara RE, Butler JC, Marston BJ, Plouffe JF, File TM, Breiman RF. Accuracy of ICD-9-CM Codes in Detecting Community-acquired Pneumococcal Pneumonia for Incidence and Vaccine Efficacy Studies. Am J Epidemiol . 1999;149(3):282-289. doi:10.1093/oxfordjournals.aje.a009804 31. Goldstein Larry B. Accuracy of ICD-9-CM Coding for the Identification of Patients With Acute Ischemic Stroke. Stroke . 1998;29(8):1602-1604. doi:10.1161/01.STR.29.8.1602 32. Tang R, Ouyang L, Li C, et al. Machine learning to parse breast pathology reports in Chinese. Breast Cancer Res Treat . 2018;169(2):243-250. doi:10.1007/s10549-018-4668-3 33. Imler TD, Morea J, Kahi C, et al. Multi-Center Colonoscopy Quality Measurement Utilizing Natural Language Processing. The American Journal of Gastroenterology . 2015;110(4):543-552. doi:10.1038/ajg.2015.51 34. Imler TD, Morea J, Kahi C, Imperiale TF. Natural Language Processing Accurately Categorizes Findings From Colonoscopy and Pathology Reports. Clinical Gastroenterology and Hepatology . 2013;11(6):689-694. doi:10.1016/j.cgh.2012.11.035 35. Jouhet V, Defossez G, Burgun A, et al. Automated Classification of Free-text Pathology Reports for Registration of Incident Cases of Cancer. Methods Inf Med . 2012;51(03):242-251. doi:10.3414/ME11-01-0005 36. Shin H-C, Roth HR, Gao M, et al. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. IEEE Transactions on Medical Imaging . 2016;35(5):1285-1298. doi:10.1109/TMI.2016.2528162