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Using multimodal mining to drive clinical guidelines development

Published in Studies in Health Technology and Informatics. 2011, vol. 169, p. 477-81
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.
Keywords AlgorithmsAnti-Bacterial Agents/economics/therapeutic useComputer SystemsDecision Support Systems, ClinicalDrug CostsHumansNational Institutes of Health (U.S.)Practice Guidelines as TopicPubMedStaphylococcus aureus/metabolismStaphylococcus epidermidis/metabolismStatistics as Topic/methodsUnited States
PMID: 21893795
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Research group Interfaces Homme-machine en milieu clinique (610)
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PASCHE, Emilie et al. Using multimodal mining to drive clinical guidelines development. In: Studies in Health Technology and Informatics, 2011, vol. 169, p. 477-81. doi: 10.3233/978-1-60750-806-9-477 https://archive-ouverte.unige.ch/unige:21586

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Deposited on : 2012-06-13

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