Abstract
A novel method of “predicting” sitter case attribute value is presented in this paper. The method allows users to choose two attributes, seed and target attribute, and to predict the target attribute value of the forthcoming sitter case. The method first retrieves string sequences of the seed attribute according to filters the users set. Then, it finds the words in the sequences and calculates the term frequencies of the words. With the term frequencies, the proposed method uses vector space model to measure the similarity between the testing sequences and the benchmark sequence. At the end, the testing sequence which has highest Cosine similarity value is chosen and the filtering value the method uses to generate the testing sequence is the predicted result. These predicted results allow hospitals to adjust their strategies on resource assignments to better handle patient needs.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: The KDD Process for Extracting Useful Knowledge from Volumes of Data. Journal of ACM Communications 39(11), 27–34 (1996)
Lin, C.-H., Hsiao, H.-S.: Hierarchical State Machine Architecture for Regular Expression Pattern Matching. In: 19th ACM Great Lakes Symposium on VLSI, Boston, MA, USA, pp. 133–136 (2009)
Jurafsky, D., Martin, J.-H.: Speech and Language Processing: An Introduction to Natural Language Processing. In: Computational Linguistics and Speech Recognition. Prentice Hall, Upper Saddle River (2000)
Zhao, Y., Karypis, G.: Evaluation of hierarchical clustering algorithms for document datasets. In: 11th International Conference on Information and Knowledge Management, McLean, VA, USA, pp. 515–524 (2002)
Augustyniak, P.: Optimal Coding of Vectorcardiographic Sequences Using Spatial Prediction. Journal of IEEE Transactions of Information Technology in Biomedicine 11(3), 305–311 (2007)
Bratsas, C., Hatzizisis, I., Bamidis, P., Quaresma, P., Maglaveras, N.: Similarity Estimation among OWL Descriptions of Computational Cardiology Problems in a Knowledge Base. Journal of IEEE Computers in Cardiology 32(5), 243–246 (2005)
Chen, C.-M., Hong, C.-M., Huang, C.-M., Lee, T.-H.: Web-based Remote Human Pulse Monitoring System with Intelligent Data Analysis for Home Healthcare. Cybernetics and Intelligent Systems, 636–641 (2008)
Subhashini, R., Kumar, V.J.S.: Evaluating the Performance of Similarity Measures Used in Document Clustering and Information Retrieval. In: 1st International Conference on Integrated Intelligent Computing, Bangalore, India, pp. 27–31 (2010)
Grishman, R.: Information Extraction: Techniques and Challenges. International Summer School on Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology, Rome, Italy, pp. 10–27 (1997)
Mutalik, P.-G., Deshpande, A., Nadkarni, P.-M.: Use of general-purpose negation detection to augment concept indexing of medical documents. Journal of the American Medical Informatics Association 8(6), 598–609 (2001)
Chapman, W.-W., Bridewell, W., Hanbury, P., Cooper, G.-F., Buchanan, B.-G.: A simple algorithm for identifying negated findings and diseases in discharge summaries. Journal of Biomedical Informatics 34(5), 301–310 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lo, S.H.K., Chang, M. (2012). An Innovative Way for Mining Clinical and Administrative Healthcare Data. In: Huang, R., Ghorbani, A.A., Pasi, G., Yamaguchi, T., Yen, N.Y., Jin, B. (eds) Active Media Technology. AMT 2012. Lecture Notes in Computer Science, vol 7669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35236-2_53
Download citation
DOI: https://doi.org/10.1007/978-3-642-35236-2_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35235-5
Online ISBN: 978-3-642-35236-2
eBook Packages: Computer ScienceComputer Science (R0)