QA-driven guidelines generation for bacteriotherapy.

TitleQA-driven guidelines generation for bacteriotherapy.
Publication TypeJournal Article
Year of Publication2009
AuthorsPasche, E, Teodoro, D, Gobeill, J, Ruch, P, Lovis, C
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Volume2009
Pagination509-13
Date Published2009
ISSN1942-597X
KeywordsAnti-Bacterial Agents, Automatic Data Processing, Bacterial Infections, Data Mining, Decision Support Techniques, Humans, Information Storage and Retrieval, Practice Guidelines as Topic, PubMed, Search Engine, Vocabulary, Controlled
Abstract

PURPOSE: We propose a question-answering (QA) driven generation approach for automatic acquisition of structured rules that can be used in a knowledge authoring tool for antibiotic prescription guidelines management.

METHODS: The rule generation is seen as a question-answering problem, where the parameters of the questions are known items of the rule (e.g. an infectious disease, caused by a given bacterium) and answers (e.g. some antibiotics) are obtained by a question-answering engine.

RESULTS: When looking for a drug given a pathogen and a disease, top-precision of 0.55 is obtained by the combination of the Boolean engine (PubMed) and the relevance-driven engine (easyIR), which means that for more than half of our evaluation benchmark at least one of the recommended antibiotics was automatically acquired by the rule generation method.

CONCLUSION: These results suggest that such an automatic text mining approach could provide a useful tool for guidelines management, by improving knowledge update and discovery.

Alternate JournalAMIA Annu Symp Proc
PubMed ID20351908