ABSTRACT
Electronic medical records (EMRs) contain clinical information about individuals. Nowadays, EMRs are widely used because of reusability and to reduce the costs associated with sharing the clinical information in healthcare sectors. Most of the EMRs are in plain text and unstructured form, hence it is difficult to filter and retrieve useful knowledge from the records. The knowledge acquired from EMR is effectively utilized by the Healthcare stakeholders. The proposed method extracts information, especially the information about diseases, using ICD-10 code which is supplemented with the statistical evidence. Here extraction of information is in the form of query combination of keywords. With the help of this method, we could generate predictions for the diseases listed in ICD-10. The predictions are based on standard data-sets from Universal Classification Irvin (UCI) repository, Word Health Organization, and EMR data-sets. The proposed method of Information Extraction and Prediction using Partial Keyword Combination and Blends Measure (IEPKCB) yields 97.4% precision, 95.0% recall, and 96.1% F-measure. The average execution time of IEPKCB is fewer than 0.84 seconds.
DISCLOSURE STATEMENT
No potential conflict of interest was reported by the author(s).
Additional information
Notes on contributors
L. Sathish Kumar
L Sathish Kumar is working as Assistant Professor, PG Department of Computer Science in Ananda College, Devakottai. He has 4.2 years teaching experience and he completed his PhD with in Department of Computer Science, Alagappa University, Karaikudi-630003, Tamil Nadu, India. He has published 10 papers in national and international conference and he published 10 papers in peer reviewed international journal.
Corresponding author. E-mail: [email protected]
A. Padmapriya
A Padmapriya, Associate Professor with Department of Computer Science, Alagappa University, Karaikudi – 630 003, Tamil Nadu, India, She has 14 years teaching experience and 10 years research experience. She published 44 papers in National and International conferences. Published more than 39 papers in national and international journals. Her research areas include data mining and network security.
E-mail: [email protected]