Joeky Senders

122 Chapter 6 REFERENCES 1. Ross MK, Wei W, Ohno-Machado L. “Big Data” and the Electronic Health Record. Yearb Med Inform. 2014;9(1):97-104. doi:10.15265/IY-2014-0003 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. 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 4. 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 5. Ostrom QT, Gittleman H, Liao P, et al. CBTRUS Statistical Report: Primary brain and other central nervous system tumors diagnosed in the United States in 2010–2014. Neuro-Oncology. 2017;19(suppl_5):v1-v88. doi:10.1093/neuonc/nox158 6. PorterMF.Analgorithmforsuffixstripping.Program.2006;40(3):211-218.doi:10.1108/00330330610681286 7. Nguyen VH, Nguyen HT, Duong HN, Snasel V. n-Gram-Based Text Compression. Comput Intell Neurosci. 2016;2016. doi:10.1155/2016/9483646 8. 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 9. Ranstam J, Cook JA. LASSO regression. BJS. 2018;105(10):1348-1348. doi:10.1002/bjs.10895 10. 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. 11. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29-36. doi:10.1148/radiology.143.1.7063747 12. Landis JR, Koch GG. The Measurement of Observer Agreement for Categorical Data. Biometrics. 1977;33(1):159-174. doi:10.2307/2529310 13. Chen L, Song L, Shao Y, Li D, Ding K. Using natural language processing to extract clinically useful information from Chinese electronic medical records. International Journal of Medical Informatics. 2019;124:6-12. doi:10.1016/j.ijmedinf.2019.01.004 14. Khor RC, Nguyen A, O’Dwyer J, et al. Extracting tumour prognostic factors from a diverse electronic record dataset in genito-urinary oncology. International Journal of Medical Informatics. 2019;121:53-57. doi:10.1016/j.ijmedinf.2018.10.008 15. Trivedi HM, Panahiazar M, Liang A, et al. Large Scale Semi-Automated Labeling of Routine Free-Text Clinical Records for Deep Learning. Journal of Digital Imaging. 2019;32(1):30-37. doi:10.1007/s10278-018- 0105-8 16. Glaser AP, Jordan BJ, Cohen J, Desai A, Silberman P, Meeks JJ. Automated Extraction of Grade, Stage, and Quality Information From Transurethral Resection of Bladder Tumor Pathology Reports Using Natural Language Processing. JCO Clinical Cancer Informatics. 2018;(2):1-8. doi:10.1200/CCI.17.00128 17. Miao S, Xu T, Wu Y, et al. Extraction of BI-RADS findings from breast ultrasound reports in Chinese using deep learning approaches. International Journal of Medical Informatics. 2018;119:17-21. doi:10.1016/j. ijmedinf.2018.08.009 18. 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

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