Intensive Care Unit Platform for Health Care Quality and Intelligent Systems Support.
ABSTRACT The underlying idea in this work consists on providing added values utilities that allow exploiting the Electronic Health
Record (EHR) as something more than a simple information record. The key for providing added value to the clinical information
systems is to exploit the synergy “Information + intelligence + ubiquity”. Based on this idea, we propose a distributed architecture
that deals with: 1) Database and an integration layer to exploit the data stored and its integration with external information
system, 2) Tools for support the medical knowledge management, 3) Tools for supervision and analysis of the health care quality
(based on EBM and Clinical Guidelines) 4) Intelligent Assistance Tools.
Conference Paper: Temporal Data Mining with Temporal Constraints.[Show abstract] [Hide abstract]
ABSTRACT: Nowadays, methods for discovering temporal knowledge try to extract more complete and representative patterns. The use of qualitative temporal constraints can be helpful in that aim, but its use should also involve methods for reasoning with them (instead of using them just as a high level representation) when a pattern consists of a constraint network instead of an isolated constraint. In this paper, we put forward a method for mining temporal patterns that makes use of a formal model for representing and reasoning with qualitative temporal constraints. Three steps should be accomplished in the method: 1) the selection of a model that allows a trade off between efficiency and representation; 2) a preprocessing step for adapting the input to the model; 3) a data mining algorithm able to deal with the properties provided by the model for generating a representative output. In order to implement this method we propose the use of the Fuzzy Temporal Constraint Network (FTCN) formalism and of a temporal abstraction method for preprocessing. Finally, the ideas of the classic methods for data mining inspire an algorithm that can generate FTCNs as output. Along this paper, we focus our attention on the data mining algorithm.Artificial Intelligence in Medicine, 11th Conference on Artificial Intelligence in Medicine, AIME 2007, Amsterdam, The Netherlands, July 7-11, 2007, Proceedings; 01/2007
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ABSTRACT: Supporting therapy selection is a fundamental task for a system for the computerized management of clinical guidelines (GL). The goal is particularly critical when no alternative is really better than the others, from a strictly clinical viewpoint. In these cases, decision theory appears to be a very suitable means to provide advice. In this paper, we describe how algorithms for calculating utility, and for evaluating the optimal policy, can be exploited to fit the GL management context.Artificial Intelligence in Medicine, 10th Conference on Artificial Intelligence in Medicine, AIME 2005, Aberdeen, UK, July 23-27, 2005, Proceedings; 01/2005
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ABSTRACT: One of the biggest issues in guideline dissemination nowadays is the need of adapting guidelines themselves to the application contexts, and to keep them up to date. In this paper, we propose a computer-based approach to facilitate the adaptation task. In particular, we focus on the management of two different levels of authors (users and supervisors), and of the history of the guideline versions.Artificial Intelligence in Medicine, 10th Conference on Artificial Intelligence in Medicine, AIME 2005, Aberdeen, UK, July 23-27, 2005, Proceedings; 01/2005