Entity Resolution for Maintaining Electronic Medical Record Using OYSTER
With the advancement of technology, the world has witnessed the digitization of various fields including education, healthcare, agriculture, manufacturing, etc. Healthcare is a very important field which has witnessed the generation of huge amounts of data in the past few decades due to a steep rise in population, and hence the ever-increasing use of online databases for storing every minute detail. Doctors no more rely on written prescriptions or documents for examining the health conditions of a patient. Electronic medical records have enabled doctors to monitor each patient’s medical history with ease. However, the rate at which the data pertaining to healthcare is increasing has led to the search for new and better alternatives that enhance the feasibility and scalability of already existing digital storage systems. This chapter is intended to provide an insight on how Entity Resolution can be put to use in healthcare for maintaining electronic medical records using the open-source software, OYSTER. Also, this chapter will throw light on how performing Entity Resolution using OYSTER has an edge over the currently used systems for storing personal medical information in hospitals.
KeywordsEntity resolution Electronic Medical Record OYSTER Identity capture Clusters Records Entities
We are grateful to Dr. John R. Talburt, Professor of Information Science at University of Arkansas, Little Rock, USA, for teaching us the concepts of entity resolution and getting handy with OYSTER. We would also like to thank him for providing us with the sample data for getting the results. Further we would like to thank Prof. Neha Katre and Prof. Vinaya Sawant, Department of Information Technology, Dwarkadas J. Sanghvi College of Engineering, Mumbai for reviewing our work and making it better and more presentable.
- 1.S.A. Asabe, N.D. Oye, M. Goji, Hospital patient database management. COMPUSOFT Int. J. Adv. Comput. Technol. 2(3), 65–73 (2013)Google Scholar
- 3.T.J. Hannan, Electronic medical record. Canad. Med. Assoc. J. 1–15 (2008)Google Scholar
- 6.I. Bhattacharya, L. Getoor, Iterative record linkage for cleaning and integration, in Proc. SIGMOD-04 DMKD Workshop, 2004Google Scholar
- 7.W. Cohen, J. Richman, Learning to match and cluster large high-dimensional data sets for data integration, in Proc. KDD-02, 2002, pp. 475–480Google Scholar
- 8.W. Cohen, P. Ravikumar, S. Fienberg. A comparison of string metrics for matching names and records, in Proc. KDD-03 Workshop on Data Cleaning, Record Linkage, and Object Consolidation, 2003, pp. 13–18Google Scholar
- 10.G. Cheng, T. Tran, Y. Qu, RELIN: relatedness and informativeness-based centrality for entity summarization, in Proceedings of the Tenth International Semantic Web Conference, Part I, ed. by L. Aroyo, C. Welty, H. Alani, J. Taylor, A. Bernstein, L. Kagal, N. F. Noy, E. Blomqvist, (Springer, Berlin, 2011), pp. 114–129Google Scholar