Joeky Senders
156 Chapter 8 References 1. Evans RS. Electronic Health Records: Then, Now, and in the Future. Yearb Med Inform . 2016;(Suppl 1):S48-S61. doi:10.15265/IYS-2016-s006 2. Matt V, Matthew H. The retrospective chart review: important methodological considerations. J Educ Eval Health Prof . 2013;10. doi:10.3352/jeehp.2013.10.12 3. Bao Y, Deng Z, Wang Y, et al. Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes. JCO Clinical Cancer Informatics . 2019;(3):1-9. doi:10.1200/CCI.19.00042 4. Senders JT, Karhade AV, Cote DJ, et al. Natural Language Processing for Automated Quantification of Brain Metastases Reported in Free-Text Radiology Reports. JCO Clinical Cancer Informatics . 2019;In Press. 5. Shi X, Yi Y, Xiong Y, et al. Extracting entities with attributes in clinical text via joint deep learning. J Am Med Inform Assoc . doi:10.1093/jamia/ocz158 6. Spandorfer A, Branch C, Sharma P, et al. Deep learning to convert unstructured CT pulmonary angiography reports into structured reports. Eur Radiol Exp . 2019;3(1):37. doi:10.1186/s41747-019-0118-1 7. Chen P-H, Zafar H, Galperin-Aizenberg M, Cook T. Integrating Natural Language Processing and Machine Learning Algorithms to Categorize Oncologic Response in Radiology Reports. J Digit Imaging . 2018;31(2):178- 184. doi:10.1007/s10278-017-0027-x 8. Bacchi Stephen, Oakden-Rayner Luke, Zerner Toby, Kleinig Timothy, Patel Sandy, Jannes Jim. Deep Learning Natural Language Processing Successfully Predicts the Cerebrovascular Cause of Transient Ischemic Attack-Like Presentations. Stroke . 2019;50(3):758-760. doi:10.1161/STROKEAHA.118.024124 9. Leyh-Bannurah S-R, Tian Z, Karakiewicz PI, et al. Deep Learning for Natural Language Processing in Urology: State-of-the-Art Automated Extraction of Detailed Pathologic Prostate Cancer Data From Narratively Written Electronic Health Records. JCO Clinical Cancer Informatics . 2018;(2):1-9. doi:10.1200/CCI.18.00080 10. Taggart M, Chapman WW, Steinberg BA, et al. Comparison of 2 Natural Language Processing Methods for Identification of Bleeding Among Critically Ill Patients. JAMA Netw Open . 2018;1(6). doi:10.1001/ jamanetworkopen.2018.3451 11. Annarumma M, Withey SJ, Bakewell RJ, Pesce E, Goh V, Montana G. Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks. Radiology . 2019;291(1):196-202. doi:10.1148/ radiol.2018180921 12. Kehl KL, Elmarakeby H, Nishino M, et al. Assessment of Deep Natural Language Processing in Ascertaining Oncologic Outcomes From Radiology Reports. JAMA Oncol . Published online July 25, 2019. doi:10.1001/ jamaoncol.2019.1800 13. Wei Q, Ji Z, Li Z, et al. A study of deep learning approaches for medication and adverse drug event extraction from clinical text. J Am Med Inform Assoc . Published online May 28, 2019. doi:10.1093/jamia/ ocz063 14. He T, Puppala M, Ezeana CF, et al. A Deep Learning-Based Decision Support Tool for Precision Risk Assessment of Breast Cancer. JCO Clin Cancer Inform . 2019;3:1-12. doi:10.1200/CCI.18.00121 15. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ . 2015;350:g7594. doi:10.1136/ bmj.g7594 16. Wu S, Roberts K, Datta S, et al. Deep learning in clinical natural language processing: a methodical review. J Am Med Inform Assoc . 2020;27(3):457-470. doi:10.1093/jamia/ocz200 17. Marshall IJ, Wallace BC. Toward systematic review automation: a practical guide to using machine learning tools in research synthesis. Syst Rev . 2019;8. doi:10.1186/s13643-019-1074-9
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