Joeky Senders

140 Chapter 7 References 1. 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 2. Mi MY, Collins JE, Lerner V, Losina E, Katz JN. Reliability of medical record abstraction by non-physicians for orthopedic research. BMC Musculoskelet Disord . 2013;14:181. doi:10.1186/1471-2474-14-181 3. Cruz CO, Meshberg EB, Shofer FS, McCusker CM, Chang AM, Hollander JE. Interrater Reliability and Accuracy of Clinicians and Trained Research Assistants Performing Prospective Data Collection in Emergency Department Patients With Potential Acute Coronary Syndrome. Annals of Emergency Medicine . 2009;54(1):1-7. doi:10.1016/j.annemergmed.2008.11.023 4. Nguyen VH, Nguyen HT, Duong HN, Snasel V. n-Gram-Based Text Compression. Comput Intell Neurosci . 2016;2016. doi:10.1155/2016/9483646 5. Jiang H, Li P, Hu X, Wang S. An improved method of term weighting for text classification. In: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems . Vol 1. ; 2009:294-298. doi:10.1109/ ICICISYS.2009.5357842 6. Banerjee I, Madhavan S, Goldman RE, Rubin DL. Intelligent Word Embeddings of Free-Text Radiology Reports. AMIA Annu Symp Proc . 2018;2017:411-420. 7. 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 8. Deep Learning for Humans. Contribute to Keras-Team/Keras Development by Creating an Account on GitHub . Keras; 2018. https://github.com/keras-team/keras. Accessed October 28, 2018. 9. Scikit-Learn: Machine Learning in Python. Contribute to Scikit-Learn/Scikit-Learn Development by Creating an Account on GitHub . scikit-learn; 2018. https://github.com/scikit-learn/scikit-learn. Accessed October 28, 2018. 10. Obermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med . 2016;375(13):1216-1219. doi:10.1056/NEJMp1606181 11. 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 12. Cheng LTE, Zheng J, Savova GK, Erickson BJ. Discerning Tumor Status from Unstructured MRI Reports— Completeness of Information in Existing Reports and Utility of Automated Natural Language Processing. J Digit Imaging . 2010;23(2):119-132. doi:10.1007/s10278-009-9215-7 13. Sada Y, Hou J, Richardson P, El-Serag H, Davila J. Validation of Case Finding Algorithms for Hepatocellular Cancer from Administrative Data and Electronic Health Records using Natural Language Processing. Med Care . 2016;54(2):e9-e14. doi:10.1097/MLR.0b013e3182a30373 14. Yim W, Denman T, Kwan SW, Yetisgen M. Tumor information extraction in radiology reports for hepatocellular carcinoma patients. AMIA Jt Summits Transl Sci Proc . 2016;2016:455-464. 15. Garla V, Taylor C, Brandt C. Semi-supervised clinical text classification with Laplacian SVMs: an application to cancer case management. J Biomed Inform . 2013;46(5):869-875. doi:10.1016/j.jbi.2013.06.014 16. Ping X-O, Tseng Y-J, Chung Y, et al. Information Extraction for Tracking Liver Cancer Patients’ Statuses: From Mixture of Clinical Narrative Report Types. Telemedicine and e-Health . 2013;19(9):704-710. doi:10.1089/ tmj.2012.0241 17. Bozkurt S, Gimenez F, Burnside ES, Gulkesen KH, Rubin DL. Using automatically extracted information from mammography reports for decision-support. J Biomed Inform . 2016;62:224-231. doi:10.1016/j. jbi.2016.07.001