Borislav Banchev
ABSTRACT
In the paper is introduced an algorithm for automated processing of documents, managed in forensic medicine offices. The algorithm is based on series of steps that are executed in a sequence in which many aspects are controlled by the information extracted from the document itself. The resulted software solution is based on adaptive case management. Using text mining techniques and meta modelling, the incoming information is transformed into knowledge that is injected into the adaptive business processes. The proposed mechanism would reduce the total turnaround time, the needed manual work at each step and the confusion for the end user.
- C. Aggarwal and C. Zhai, Mining Text Data, 1st ed., vol. 4. Boston, MA: Springer US, 2012. Google Scholar
Digital Library
- B. Banchev and T. Georgiev, "Architecture of automated communication and data processing system for forensic medicine," in Proceedings of the 16th International Conference on Computer Systems and Technologies - CompSysTech '15, 2015, pp. 160--167. Google ScholarDigital Library
- I. Bider, T. Halpin, J. Krogstie, S. Nurcan, E. Proper, R. Schmidt, and R. Ukor, Eds., Enterprise, Business-Process and Information Systems Modeling, vol. 50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. Google ScholarDigital Library
- F. Campos and A. Valencia, New Contributions in Information Systems and Technologies, vol. 353. 2015.Google Scholar
- G. Georgiev, P. Nakov, and K. Ganchev, "Feature-Rich Named Entity Recognition for Bulgarian Using Conditional Random Fields," pp. 113--117, 2009.Google Scholar
- M. Henke, E. Perjons, and E. Sneiders, "Supporting workflow and adaptive case management with language technologies," in Advances in Intelligent Systems and Computing, 2015, vol. 353, pp. 543--552.Google ScholarCross Ref
- J. Huang, J. Lu, and C. X. Ling, "Comparing naive Bayes, decision trees, and SVM with AUC and accuracy," Third IEEE Int. Conf. Data Min., pp. 11--14, 2003. Google ScholarDigital Library
- D. Hull and J. Pedersen, "Document Routing as Statistical Classification," AAAI Tech. Rep., pp. 49--53, 1996.Google Scholar
- T. Joachims, "Text Categorization with Suport Vector Machines: Learning with Many Relevant Features," Proc. 10th Eur. Conf. Mach. Learn. ECML '98, vol. 1398, pp. 137--142, 1998. Google ScholarDigital Library
- C. D. Manning, P. Raghavan, and H. Schutze, An Introduction to Information Retrieval. Cambridge UP, 2009. Google Scholar
- Object Management Group (OMG), "BPMN, CMMN AND DMN SPECIFICATIONS AT OMG.".Google Scholar
- Object Management Group (OMG), "Case Management Model and Notation," no. May, p. 82, 2014.Google Scholar
- T. Poibeau, H. Saggion, J. Piskorski, and R. Yangarber, Eds., Multi-source, Multilingual Information Extraction and Summarization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. Google ScholarDigital Library
- F. Sebastiani, "Machine learning in automated text categorization," ACM Comput. Surv., vol. 34, no. 1, pp. 1--47, Mar. 2002. Google ScholarDigital Library
- L. Smiers, M. Deb, J. Koster, and P. Palvankar, Oracle Case Management Solutions. CRC Press, 2015. Google ScholarDigital Library
- R. Srihari, C. Niu, and W. Li, "A Hybrid Approach for Named Entity and Sub-Type Tagging," Appl. Nat. Lang. Process. Conf., pp. 247--254, 2000. Google ScholarDigital Library
- A. Tan, "Text Categorization, Supervised Learning, and Domain Knowledge Integration," Neural Networks.Google Scholar
- D. Wang and H. Zhang, "Inverse-category-frequency based supervised term weighting schemes for text categorization," J. Inf. Sci. Eng., vol. 29, no. 2, pp. 209--225, 2013.Google Scholar
- K. Yi and J. Beheshti, "A hidden Markov model-based text classification of medical documents," J. Inf. Sci., vol. 35, no. 1, pp. 67--81, Jul. 2008. Google ScholarDigital Library
Recommendations
An adaptive case management system to support integrated care services
Display Omitted The Adaptive Case Management (ACM) system supports deployment of Integrated Care (IC).The open-source library of integrated care protocols facilitates case management.Case management has been conceptualized in five main stages.NEXES ...
Manual and evolutionary equalization in text mining
SMO'07: Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and OptimizationThe phase of Text Retrieval or Information Extraction may represent a weakness for the entire text mining process. During this phase, keywords are extracted that can be used in documents classification or clustering. If the extracted keywords are not ...
Seeding the survey and analysis of research literature with text mining
Text mining is a semi-automated process of extracting knowledge from a large amount of unstructured data. Given that the amount of unstructured data being generated and stored is increasing rapidly, the need for automated means to process it is also ...
Comments