QA-driven guidelines generation for bacteriotherapy.
| Title | QA-driven guidelines generation for bacteriotherapy. |
| Publication Type | Journal Article |
| Year of Publication | 2009 |
| Authors | Pasche, E, Teodoro, D, Gobeill, J, Ruch, P, Lovis, C |
| Journal | AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium |
| Volume | 2009 |
| Pagination | 509-13 |
| Date Published | 2009 |
| ISSN | 1942-597X |
| Keywords | Anti-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 Journal | AMIA Annu Symp Proc |
| PubMed ID | 20351908 |
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