Abstract
We aim at proposing a rule generation approach to automatically acquire structured rules that can be used in decision support systems for drug prescription. We apply a question-answering engine to answer specific information requests. The rule generation is seen as an equation problem, where the factors are known items of the rule (e.g., an infectious disease, caused by a given bacteria) and solutions are answered by the engine (e.g., some antibiotics). A top precision of 0.64 is reported, which means, for about two third of the knowledge rules of the benchmark, one of the recommended antibiotic was automatically acquired by the rule generation method. These results suggest that a significant fraction of the medical knowledge can be obtained by such an automatic text mining approach.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
