Title :
Relational learning from drug adverse events reports
Author :
Biçici, Ergun M.
Author_Institution :
Dept. of Comput. Sci., North Carolina State Univ., Raleigh, NC, USA
Abstract :
We applied relational learning to discover rules from adverse events reports. We used the FOIL relational learning system to find a set of rules for withdrawn drugs. We compared our results with FDA´s reasons for withdrawal.
Keywords :
drugs; learning (artificial intelligence); medical computing; FOIL relational learning system; drug adverse events reports; withdrawn drugs; Computer science; Demography; Drugs; Frequency; Industrial relations; Learning systems; Logic; Machine learning; Ontologies; Pharmaceuticals;
Conference_Titel :
Biotechnology and Bioinformatics, 2004. Proceedings. Technology for Life: North Carolina Symposium on
Print_ISBN :
0-7803-8826-7
DOI :
10.1109/SBB.2004.1364375
