A novel semi-supervised algorithm for rare prescription side effect discovery

Tools

Reps, Jenna M., Garibaldi, Jonathan M., Aickelin, Uwe, Soria, Daniele, Gibson, Jack E. and Hubbard, Richard B. (2014) A novel semi-supervised algorithm for rare prescription side effect discovery. IEEE Journal of Biomedical and Health Informatics, 18 (2). pp. 537-547. ISSN 2168-2194

Full text not available from this repository.
Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/996697
Additional Information: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Biomedical Informatics, Data Mining
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: 10.1109/JBHI.2013.2281505
Depositing User: Aickelin, Professor Uwe
Date Deposited: 26 Sep 2014 21:34
Last Modified: 04 May 2020 20:15
URI: https://eprints.nottingham.ac.uk/id/eprint/3355

Actions (Archive Staff Only)

Edit View Edit View