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References_list.txt
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References_list.txt
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1. https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2018.html
2. Fritz AG, Jack A, Parkin D, Percy C, Shanmugarathan S, Sobin L, et al. International classification of diseases
for oncology: ICD-O, Third Edition. World Health Organization; 2000.
3.Adamo MB, Johnson CH, Ruhl JL, Dickie LA. SEER Program Coding and Staging Manual 2012. National
Cancer Institute, NIH Publication number 12-5581; 2012.
4. Joachims, Thorsten & Ls, Informatik & Str, Baroper. (1999). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. .
5.Y. Ng, Andrew & Jordan, Michael. (2002). On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes. Adv. Neural Inf. Process. Sys. 2.
6.Peng, Fuchun & Schuurmans, Dale. (2002). Combining Naive Bayes and n-Gram Language Models for Text Classification. Advances in Information Retrieval. 2633. 10.1007/3-540-36618-0_24.
7..Anni Coden, Guergana Savova, Igor Sominsky, Michael Tanenblatt, James Masanz, Karin Schuler, James Cooper, Wei Guan, and Piet C de Groen. 2009. Automatically extracting cancer disease characteristics from pathology reports into a disease knowledge representation model. Journal of Biomedical Informatics, 42:937–949.
8.Cheng, Lionel & Zheng, Jiaping & K Savova, Guergana & Erickson, Bradley. (2009). Discerning Tumor Status from Unstructured MRI Reports—Completeness of Information in Existing Reports and Utility of Automated Natural Language Processing. Journal of digital imaging : the official journal of the Society for Computer Applications in Radiology. 23. 119-32. 10.1007/s10278-009-9215-7.
9.Li, Yue & Martinez, David. (2010). Information Extraction of Multiple Categories from Pathology Reports.
10. Anthony N Nguyen, Michael J Lawley, David P Hansen, Rayleen V Bowman, Belinda E Clarke, Edwina E Duhig, and Shoni Colquist. 2010. Symbolic rule-based classification of lung cancer stages from free-text pathology reports. Journal of the American Medical Informatics Association (JAMIA), 17:440–445.
11.Cunningham H, Maynard D, Bontcheva K, et al. GATE: a framework and graphical
development environment for robust NLP tools and applications. Proceedings of the
40th Anniversary Meeting of the Association for Computational Linguistics; July 2002,
Philadelphia; 2002.
12.International Health Terminology Standards Development Organisation.
SNOMED Clinical Terms User Guide. 2008. http://www.ihtsdo.org (accessed
Sep 2008).
13.Garla, Vijay & Taylor, Caroline & Brandt, Cynthia. (2013). Semi-supervised clinical text classification with Laplacian SVMs: An application to cancer case management. Journal of biomedical informatics. 42. 10.1016/j.jbi.2013.06.014.
14. Kavuluru, Ramakanth & Hands, Isaac & B Durbin, Eric & Witt, Lisa. (2013). Automatic Extraction of ICD-O-3 Primary Sites from Cancer Pathology Reports. AMIA Summits on Translational Science proceedings AMIA Summit on Translational Science. 2013. 112-116.
15. Kalchbrenner, Nal & Grefenstette, Edward & Blunsom, Phil. (2014). A Convolutional Neural Network for Modelling Sentences. 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference. 1. 10.3115/v1/P14-1062.
16.Kim, Yoon. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 10.3115/v1/D14-1181.
17. Oleynik, Michel & Patrão, Diogo & Finger, Marcelo. (2015). Automated Classification of Pathology Reports. Studies in health technology and informatics. 216. 10.3233/978-1-61499-564-7-1040.
18. Oleynik, Michel & Patrão, Diogo & Finger, Marcelo. (2017). Automated Classification of Semi-Structured Pathology Reports into ICD-O Using SVM in Portuguese. Studies in health technology and informatics. 235. 10.3233/978-1-61499-753-5-256.
19. V. Jouhet; G. Defossez; A. Burgun; P. le Beux; P.Levillain,; P. Ingrand1; V. Claveau. Automated Classification of Free-text Pathology Reports for Registration of Incident Cases of Cancer. Methods Inf Med 2012; 51: 242 – 251 doi:0.3414/ME11-01-000
20. Nguyen A, Moore J, Zuccon G, Lawley M, Colquist S. Classification of pathology reports for cancer registry notifications. Studies in Health Technology and Informatics, Pgs 150-156, doi:10.3233/978-1-61499-078-9-150
22.Löpprich M1, Krauss F, Ganzinger M, Senghas K, Riezler S, Knaup P. Automated Classification of Selected Data Elements from Free-text Diagnostic Reports for Clinical Research. Methods Inf Med 2016; 55(04): 373-380 . DOI: 10.3414/ME15-02-0019
23.Li, Peng & Huang, Heng. (2016). Clinical Information Extraction via Convolutional Neural Network.
24.N Jagannatha, Abhyuday & Yu, Hong. (2016). Bidirectional RNN for Medical Event Detection in Electronic Health Records. Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting. 2016. 473-482.
25. Mujtaba G, Shuib L, Raj RG, Rajandram R, Shaikh K, Al-Garadi MA (2017) Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection. PLoS ONE 12(2): e0170242. doi:10.1371/journal.pone.0170242
26.Qui et. al., Deep Learning for Automated Extraction of Primary Sites From Cancer Pathology Reports,IEEE Journal of Biomedical and Health Informatics, Vol. 22, No. 1, January 2018
27.Yang, Zichao & Yang, Diyi & Dyer, Chris & He, Xiaodong & Smola, Alex & Hovy, Eduard. (2016). Hierarchical Attention Networks for Document Classification. 1480-1489. 10.18653/v1/N16-1174.
28. Gao,Shang & Young,Michael T & Qiu,John X & Hong-Jun Yoon & James B & Christian Fearn & Paul A & Tourassi,Georgia D & Arvind Ramanathan.Hierarchical attention networks for information extraction from cancer pathology reports . Journal of the American Medical Informatics Association, Volume 25, Issue 3, 1 March 2018, Pages 321–330, https://doi.org/10.1093/jamia/ocx131
------------------original refernces:
11. Friedman, C., Alderson, P. O., Austin, J. H., Cimino, J. J., & Johnson, S. B. (1994). A general natural-language text processor for clinical radiology. Journal of the American Medical Informatics Association, 1(2), 161-174.
54. Friedman, C., Alderson, P. O., Austin, J. H., Cimino, J. J., & Johnson, S. B. (1994). A general natural-language text processor for clinical radiology. Journal of the American Medical Informatics Association, 1(2), 161-174.
15. Hripcsak, G., Friedman, C., Alderson, P. O., DuMouchel, W., Johnson, S. B., & Clayton, P. D. (1995). Unlocking clinical data from narrative reports: a study of natural language processing. Annals of internal medicine, 122(9), 681-688.
1.Joachims, T. (1996). A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization (No. CMU-CS-96-118). Carnegie-mellon univ pittsburgh pa dept of computer science.
10.Bull, A. D., Biffin, A. H., Mella, J., Radcliffe, A. G., Stamatakis, J. D., Steele, R. J., & Williams, G. T. (1997). Colorectal cancer pathology reporting: a regional audit. Journal of clinical pathology, 50(2), 138-142.
2.Joachims, T. (1998, April). Text categorization with support vector machines: Learning with many relevant features. In European conference on machine learning (pp. 137-142). Springer, Berlin, Heidelberg.
61.Nigam, K., Lafferty, J., & McCallum, A. (1999, August). Using maximum entropy for text classification. In IJCAI-99 workshop on machine learning for information filtering (Vol. 1, pp. 61-67).
66. Furey, T. S., Cristianini, N., Duffy, N., Bednarski, D. W., Schummer, M., & Haussler, D. (2000). Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 16(10), 906-914.
57. Friedman, C. (2000). A broad-coverage natural language processing system. In Proceedings of the AMIA Symposium(p. 270). American Medical Informatics Association.
62. Lafferty, J., McCallum, A., & Pereira, F. C. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data.
63.Stearns, M. Q., Price, C., Spackman, K. A., & Wang, A. Y. (2001). SNOMED clinical terms: overview of the development process and project status. In Proceedings of the AMIA Symposium (p. 662). American Medical Informatics Association.
14. Hripcsak, G., Austin, J. H., Alderson, P. O., & Friedman, C. (2002). Use of natural language processing to translate clinical information from a database of 889,921 chest radiographic reports. Radiology, 224(1), 157-163.
56. Cunningham, H. (2002). GATE, a general architecture for text engineering. Computers and the Humanities, 36(2), 223-254.
9. Branston, L. K., Greening, S., Newcombe, R. G., Daoud, R., Abraham, J. M., Wood, F., ... & Williams, G. T. (2002). The implementation of guidelines and computerised forms improves the completeness of cancer pathology reporting. The CROPS project: a randomised controlled trial in pathology. European Journal of Cancer, 38(6), 764-772
8. Wilkinson, N. W., Shahryarinejad, A., Winston, J. S., Watroba, N., & Edge, S. B. (2003). Concordance with breast cancer pathology reporting practice guidelines. Journal of the American College of Surgeons, 196(1), 38-43.
7. Beattie, G. C., McAdam, T. K., Elliott, S., Sloan, J. M., & Irwin, S. T. (2003). Improvement in quality of colorectal cancer pathology reporting with a standardized proforma–a comparative study. Colorectal disease, 5(6), 558-562.
55.Ferrucci, D., & Lally, A. (2004). UIMA: an architectural approach to unstructured information processing in the corporate research environment. Natural Language Engineering, 10(3-4), 327-348.
72. Fritz, A.G., Jack, A., Parkin, D., Percy, C., Shanmugarathan, S., Sobin, L., et al. (2000). International classification of diseases for oncology: ICD-O . Third Edition . World Health Organization.
5.Xu, H., Anderson, K., Grann, V. R., & Friedman, C. (2004). Facilitating cancer research using natural language processing of pathology reports. Studies in health technology and informatics, 107(Pt 1), 565-572.
6.Rubin, M. A., Bismar, T. A., Curtis, S., & Montie, J. E. (2004). Prostate needle biopsy reporting: how are the surgical members of the Society of Urologic Oncology using pathology reports to guide treatment of prostate cancer patients?. The American journal of surgical pathology, 28(7), 946-952.
13. Friedman, C., Shagina, L., Lussier, Y., & Hripcsak, G. (2004). Automated encoding of clinical documents based on natural language processing. Journal of the American Medical Informatics Association, 11(5), 392-402.
52. McCowan, I. A., Moore, D. C., Nguyen, A. N., Bowman, R. V., Clarke, B. E., Duhig, E. E., & Fry, M. J. (2007). Collection of cancer stage data by classifying free-text medical reports. Journal of the American Medical Informatics Association, 14(6), 736-745.
19. Savova, G., Kipper-Schuler, K., Buntrock, J., & Chute, C. (2008). UIMA-based clinical information extraction system. Towards enhanced interoperability for large HLT systems: UIMA for NLP, 39.
22. Leaman, R., & Gonzalez, G. (2008). BANNER: an executable survey of advances in biomedical named entity recognition. In Biocomputing 2008 (pp. 652-663).
45. Meystre, S. M., Savova, G. K., Kipper-Schuler, K. C., & Hurdle, J. F. (2008). Extracting information from textual documents in the electronic health record: a review of recent research. Yearbook of medical informatics, 17(01), 128-144.
47. Roberts, A., Gaizauskas, R., Hepple, M., Demetriou, G., Guo, Y., Roberts, I., & Setzer, A. (2009). Building a semantically annotated corpus of clinical texts. Journal of biomedical informatics, 42(5), 950-966.
21. Mykowiecka, A., Marciniak, M., & Kupść, A. (2009). Rule-based information extraction from patients’ clinical data. Journal of biomedical informatics, 42(5), 923-936.
18. Srigley, J. R., McGowan, T., MacLean, A., Raby, M., Ross, J., Kramer, S., & Sawka, C. (2009). Standardized synoptic cancer pathology reporting: A population‐based approach. Journal of surgical oncology, 99(8), 517-524.
20. Coden, A., Savova, G., Sominsky, I., Tanenblatt, M., Masanz, J., Schuler, K., ... & de Groen, P. C. (2009). Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model. Journal of biomedical informatics, 42(5), 937-949.
69. El Naqa, I., Grigsby, P. W., Apte, A., Kidd, E., Donnelly, E., Khullar, D., ... & Thorstad, W. L. (2009). Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern recognition, 42(6), 1162-1171.
53. Savova, G. K., Masanz, J. J., Ogren, P. V., Zheng, J., Sohn, S., Kipper-Schuler, K. C., & Chute, C. G. (2010). Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. Journal of the American Medical Informatics Association, 17(5), 507-513.
12. Crowley, R. S., Castine, M., Mitchell, K., Chavan, G., McSherry, T., & Feldman, M. (2010). caTIES: a grid based system for coding and retrieval of surgical pathology reports and tissue specimens in support of translational research. Journal of the American Medical Informatics Association, 17(3), 253-264.
16. D'avolio, L. W., Nguyen, T. M., Farwell, W. R., Chen, Y., Fitzmeyer, F., Harris, O. M., & Fiore, L. D. (2010). Evaluation of a generalizable approach to clinical information retrieval using the automated retrieval console (ARC). Journal of the American Medical Informatics Association, 17(4), 375-382.
42. Cheng, L. T., Zheng, J., Savova, G. K., & Erickson, B. J. (2010). Discerning tumor status from unstructured MRI reports—completeness of information in existing reports and utility of automated natural language processing. Journal of digital imaging, 23(2), 119-132.
51. Nguyen, A. N., Lawley, M. J., Hansen, D. P., Bowman, R. V., Clarke, B. E., Duhig, E. E., & Colquist, S. (2010). Symbolic rule-based classification of lung cancer stages from free-text pathology reports. Journal of the American Medical Informatics Association, 17(4), 440-445.
37. Nguyen, A., Moore, J., Lawley, M., Hansen, D., & Colquist, S. (2011). Automatic extraction of cancer characteristics from free-text pathology reports for cancer notifications. Studies in health technology and informatics, 168, 117-124.
43. Martinez, D., & Li, Y. (2011, October). Information extraction from pathology reports in a hospital setting. In Proceedings of the 20th ACM international conference on Information and knowledge management (pp. 1877-1882). ACM.
50. Xu, H., Fu, Z., Shah, A., Chen, Y., Peterson, N. B., Chen, Q., ... & Denny, J. C. (2011). Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases. In AMIA Annual Symposium Proceedings (Vol. 2011, p. 1564). American Medical Informatics Association.
34. Jouhet, V., Defossez, G., Burgun, A., Le Beux, P., Levillain, P., Ingrand, P., & Claveau, V. (2012). Automated classification of free-text pathology reports for registration of incident cases of cancer. Methods of information in medicine, 51(3), 242.
35. LAWLEY, M., & COLQUIST, S. (2012, July). Classification of pathology reports for cancer registry notifications. In Health Informatics: Building a Healthcare Future Through Trusted Information: Selected Papers from the 20th Australian National Health Informatics Conference (HIC 2012) (Vol. 178, p. 150). IOS Press.
59. Wang, T., Wu, D. J., Coates, A., & Ng, A. Y. (2012, November). End-to-end text recognition with convolutional neural networks. In Pattern Recognition (ICPR), 2012 21st International Conference on (pp. 3304-3308). IEEE.
23. Leaman, R., Islamaj Doğan, R., & Lu, Z. (2013). DNorm: disease name normalization with pairwise learning to rank. Bioinformatics, 29(22), 2909-2917.
39. Garla, V., Taylor, C., & Brandt, C. (2013). Semi-supervised clinical text classification with Laplacian SVMs: an application to cancer case management. Journal of biomedical informatics, 46(5), 869-875.
40. Kavuluru, R., Hands, I., Durbin, E. B., & Witt, L. (2013). Automatic extraction of ICD-O-3 primary sites from cancer pathology reports. AMIA Summits on Translational Science Proceedings, 2013, 112.
41. Lakshmaiah, K. C., Guruprasad, B., Lokesh, K. N., & Veena, V. S. (2014). Cancer notification in India. South Asian journal of cancer, 3(1), 74.
48. Spasić, I., Livsey, J., Keane, J. A., & Nenadić, G. (2014). Text mining of cancer-related information: review of current status and future directions. International journal of medical informatics, 83(9), 605-623.
3.Kalchbrenner, Nal & Grefenstette, Edward & Blunsom, Phil. (2014). A Convolutional Neural Network for Modelling Sentences. 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference. 10.3115/v1/P14-1062.
4.Kim, Yoon. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 10.3115/v1/D14-1181
29. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473
60.Pennington, J., Socher, R., & Manning, C. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).
17. Hanauer, D. A., Mei, Q., Law, J., Khanna, R., & Zheng, K. (2015). Supporting information retrieval from electronic health records: A report of University of Michigan’s nine-year experience in developing and using the Electronic Medical Record Search Engine (EMERSE). Journal of biomedical informatics, 55, 290-300.
65. Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal, 13, 8-17.
21+1. Leaman, R., Khare, R., & Lu, Z. (2015). Challenges in clinical natural language processing for automated disorder normalization. Journal of biomedical informatics, 57, 28-37.
58. Zhang, X., Zhao, J., & LeCun, Y. (2015). Character-level convolutional networks for text classification. In Advances in neural information processing systems (pp. 649-657).
33. Oleynik, M., Finger, M., & Patrão, D. F. (2015). Automated Classification of Pathology Reports. In MedInfo (p. 1040).
27. Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., & Hovy, E. (2016). Hierarchical attention networks for document classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies(pp. 1480-1489).
36. Löpprich, M., Krauss, F., Ganzinger, M., Senghas, K., Riezler, S., & Knaup, P. (2016). Automated classification of selected data elements from free-text diagnostic reports for clinical research. Methods of information in medicine, 55(04), 373-380.
70. Strickland-Marmol, L. B., Muro-Cacho, C. A., Barnett, S. D., Banas, M. R., & Foulis, P. R. (2016). College of American Pathologists cancer protocols: Optimizing format for accuracy and efficiency. Archives of pathology & laboratory medicine, 140(6), 578-587.
38. Li, P., & Huang, H. (2016). Clinical information extraction via convolutional neural network. arXiv preprint arXiv:1603.09381.
44. Napolitano, G., Marshall, A., Hamilton, P., & Gavin, A. T. (2016). Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction. Artificial intelligence in medicine, 70, 77-83.
30. Jagannatha, A. N., & Yu, H. (2016, June). Bidirectional RNN for medical event detection in electronic health records. In Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting (Vol. 2016, p. 473). NIH Public Access.
28. Gao, S., Young, M. T., Qiu, J. X., Yoon, H. J., Christian, J. B., Fearn, P. A., ... & Ramanthan, A. (2017). Hierarchical attention networks for information extraction from cancer pathology reports. Journal of the American Medical Informatics Association, 25(3), 321-330.
31. Mujtaba, G., Shuib, L., Raj, R. G., Rajandram, R., Shaikh, K., & Al-Garadi, M. A. (2017). Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection. PloS one, 12(2), e0170242.
24. Tao, C., Filannino, M., & Uzuner, Ö. (2017). Prescription extraction using CRFs and word embeddings. Journal of biomedical informatics, 72, 60-66.
26. Qiu, J., Yoon, H. J., Fearn, P. A., & Tourassi, G. D. (2017). Deep learning for automated extraction of primary sites from cancer pathology reports. IEEE journal of biomedical and health informatics, 22(1).
32. Oleynik, M., Patrão, D. F., & Finger, M. (2017). Automated Classification of Semi-Structured Pathology Reports into ICD-O Using SVM in Portuguese. Studies in health technology and informatics, 235, 256-260.
46. Yala, A., Barzilay, R., Salama, L., Griffin, M., Sollender, G., Bardia, A., ... & Garber, J. E. (2017). Using machine learning to parse breast pathology reports. Breast cancer research and treatment, 161(2), 203-211.
49. Wang, Y., Wang, L., Rastegar-Mojarad, M., Moon, S., Shen, F., Afzal, N., ... & Liu, H. (2017). Clinical information extraction applications: a literature review. Journal of biomedical informatics.
67. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115.
68. Kadurin, A., Aliper, A., Kazennov, A., Mamoshina, P., Vanhaelen, Q., Khrabrov, K., & Zhavoronkov, A. (2017). The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget, 8(7), 10883.
25. Banerjee, I., Chen, M. C., Lungren, M. P., & Rubin, D. L. (2018). Radiology report annotation using intelligent word embeddings: Applied to multi-institutional chest CT cohort. Journal of biomedical informatics, 77, 11-20.
71.Cancer Committee, College of American Pathologists. Current Cancer Protocols. (2018). http://outage.cap.org/Current_Cancer_Protocols_2018_01.zip. Accessed on: 2018-08-05.
64.American Cancer Society, Inc. (2018). Available online at: https://www.cancer.org/research/cancer-facts-statistics/global.html . Accessed on: 2018-08-04.