title: Using multimodal mining to drive clinical guidelines development.
abstract: We present exploratory investigations of multimodal mining to help designing clinical guidelines for antibiotherapy. Our approach is based on the assumption that combining various sources of data, such as the literature, a clinical datawarehouse, as well as information regarding costs will result in better recommendations. Compared to our baseline recommendation system based on a question-answering engine built on top of PubMed, an improvement of +16% is observed when clinical data (i.e. resistance profiles) are injected into the model. In complement to PubMed, an alternative search strategy is reported, which is significantly improved by the use of the combined multimodal approach. These results suggest that combining literature-based discovery with structured data mining can significantly improve effectiveness of decision-support systems for authors of clinical practice guidelines.
authors: BiTeM Group, Geneva, Switzerland. emilie.pasche@unige.ch
authors: Emilie
authors: E
authors: Pasche
authors: Julien
authors: J
authors: Gobeill
authors: Douglas
authors: D
authors: Teodoro
authors: Dina
authors: D
authors: Vishnyakova
authors: Arnaud
authors: A
authors: Gaudinat
authors: Patrick
authors: P
authors: Ruch
authors: Christian
authors: C
authors: Lovis
mesh_terms: D000465:Algorithms; D000900:Anti-Bacterial Agents / Q000191:economics / Q000627:therapeutic use*; D003199:Computer Systems; D020000:Decision Support Systems, Clinical; D016527:Drug Costs; D006801:Humans; D009316:National Institutes of Health (U.S.); D017410:Practice Guidelines as Topic; D039781:PubMed; D013211:Staphylococcus aureus / Q000378:metabolism; D013212:Staphylococcus epidermidis / Q000378:metabolism; D013223:Statistics as Topic / Q000379:methods*; D014481:United States
chemical_list: D000900:Anti-Bacterial Agents