Abstract
From a technological point of view two kinds of requirements must be taken into account when implementing Clinical Decision Support Systems (CDSSs) for antibiotic surveillance in a hospital. First, Artificial Intelligence (AI) technologies are usually applied to represent and reason about existing clinical knowledge, but also to discover new one from raw data. Second, at a global decision level, representative applications of Business Intelligence (BI) must be also considered. The present work introduces the design and implementation of a CDSS platform that integrates both AI and BI technologies to assist clinicians in the rational use of antibiotics in a hospital. The choice of a Hadoop based Big Data architecture provides a suitable solution for the problem of integrating, processing and analysing large sets of clinical data. The platform facilitates the daily follow-up of antibiotic therapies and infections while offering various decision support modules at both patient and global level. The system is being tested and evaluated in a university hospital.
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Acknowledgments
This work was partially funded by the Spanish Ministry of Economy and Competitiveness under the WASPSS project (Ref: TIN2013-45491-R) and by European Fund for Regional Development (EFRD, FEDER).
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Morales, A., Cánovas-Segura, B., Campos, M., Juarez, J.M., Palacios, F. (2016). Proposal of a Big Data Platform for Intelligent Antibiotic Surveillance in a Hospital. In: Luaces , O., et al. Advances in Artificial Intelligence. CAEPIA 2016. Lecture Notes in Computer Science(), vol 9868. Springer, Cham. https://doi.org/10.1007/978-3-319-44636-3_24
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DOI: https://doi.org/10.1007/978-3-319-44636-3_24
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