Instant Answering For Health Seekers Using Machine Learning
Keywords:
Machine Learning, NLP, Support Vector MachineAbstract
To bridge the vocabulary gap between health seekers and providers is to code the medical records by jointly utilizing local mining and global learning. The emerging community generated health data is more colloquial in terms of inconsistency, complexity and ambiguity is overcome by machine learning process. Machine learning is achieved by using local mining and global learning techniques. Local mining database gets updated by global learning data. Global learning comprises a large collection of medical resources in its backend which helps to retrieve a related resource to the query based on terminology keywords.
References
[1] L. Nie, M. Akbari, T. Li, and T.-S. Chua, “A joint local-global approach for medical terminology assignment,†in Proc. Int. ACM SIGIR Conf., 2014.
[2] L. Nie, T. Li, M. Akbari, and T.-S. Chua, “Wenzher: Comprehensive vertical search for healthcare domain,†in Proc. Int. ACM SIGIR Conf., 2014, pp. 1245–1246.
[3] AHIMA e-HIM Work Group on Computer-Assisted Coding, “Delving into computer-assisted coding,†J. AHIMA, vol. 75, pp. 48A–48H, 2004.
[4] G. Leroy and H. Chen, “Meeting medical terminology needs-the ontology-enhanced medical concept mapper,†IEEE Trans. Inf. Technol. Biomed., vol. 5, no. 4, pp. 261–270, Dec. 2001.
[5] G. Zuccon, B. Koopman, A. Nguyen, D. Vickers, and L. Butt, “Exploiting medical hierarchies for concept-based information retrieval,†in Proc. Australasian Document Comput. Symp., 2012, pp. 111–114.
[6] E. J. M. Laur_ıa and A. D. March, “Combining Bayesian text classification and shrinkage to automate healthcare coding: A data quality analysis,†J. Data Inf. Quart., vol. 2, no. 3, p. 13, 2011.
[7] L. Yves A., S. Lyudmila, and F. Carol, “Automating ICD-9-cm encoding using medical language processing: A feasibility study,†in Proc. AMIA Annu. Symp., 2000, p. 1072.
[8] C. Dozier, R. Kondadadi, K. Al-Kofahi, M. Chaudhary, and X. Guo, “Fast tagging of medical terms in legal text,†in Proc. Int. Conf. Artif. Intell. Law, 2007, pp. 253–260.
[9] L. V. Lita, S. Yu, S. Niculescu, and J. Bi, “Large scale diagnostic code classification for medical patient records,†in Proc. Conf. Artif. Intell. Med., 1995.
[10] L. S. Larkey and W. B. Croft, “Automatic assignment of icd9 codes to discharge summaries,†PhD dissertation, Dept. Comput. Sci., Univ. Massachusetts at Amherst, Amherst, MA, USA, 1995.
[11] H. Suominen, F. Ginter, S. Pyysalo, A. Airola, T. Pahikkala, S. Salanter, and T. Salakoski, “Machine learning to automate the assignment of diagnosis codes to free-text radiology reports: A method description,†in Proc. ICML Workshop Mach. Learn. Health-Care Appl., 2008.
[12] J. Patrick, Y. Wang, and P. Budd, “An automated system for conversion of clinical notes into snomed clinical terminology,†in Proc. 5th Australasian Symp. ACSW Frontiers, 2007, pp. 219–226.
[13] W. R. Hersh and H. David, “Information retrieval in medicine: The saphire experience,†J. Amer. Soc. Inf. Sci., vol. 46, no. 10, pp. 743–747, 1995.
[14] Q. Zhou, W. W. Chu, C. Morioka, G. H. Leazer, and H. Kangarloo, “Indexfinder: A method of extracting key concepts from clinical texts for indexing,†in Proc. AMIA Annu. Symp., 2003, pp. 763–767.
[15] Y. Wang and J. Patrick, “Mapping clinical notes to medical terminology at point of care,†in Proc. Workshop Current Trends Biomed. Natural Lang. Process., 2008, pp. 102–103.
[16] S. Hina, E. Atwell, and O. Johnson, “Semantic tagging of medical narratives with top level concepts from SNOMED CT healthcare data standard,†Int. J. Intell. Comput. Res., vol. 2, pp. 204–210, 2010.
[17] H. Stenzhorn, E. Pacheco, P. Nohama, and S. Schulz, “Automatic mapping of clinical documentation to SNOMED CT,†Studies Health Technol. Inform., vol. 158, pp. 228–232, 2009.
[18] K. Crammer, M. Dredze, K. Ganchev, P. P. Talukdar, and S. Carroll, “Automatic code assignment to medical text,†in Proc. Workshop Biol., Translational, Clinical Lang. Process., 2007, pp. 129–136.
[19] L. R. S. de Lima, A. H. F. Laender, and B. A. Ribeiro-Neto, “A hierarchical approach to the automatic categorization of medical documents,†in Proc. Int. Conf. Inf. Knowl. Manag., 1998, pp. 132–139.
[20] Y. Yan, G. Fung, J. G. Dy, and R. Rosales, “Medical coding classification by leveraging inter-code relationships,†in Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2012, pp. 193–202.
[21] S. V. Pakhomov, J. D. Buntrock, and C. G. Chute, “Automating the assignment of diagnosis codes to patient encounters using example- based and machine learning techniques,†J. Amer. Med. Inf. Assoc., vol. 13, no. 5, pp. 516–525, 2006.
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