Proceedings of the Real-Time: Theory in Practice, REX Workshop
Temporal Constraints Approximation from Data about Medical Procedures
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
First Approach to Micro-temporality Generation for Clinical Algorithms
Proceedings of the 2010 conference on Information Modelling and Knowledge Bases XXI
Automatic generation of clinical algorithms within the state-decision-action model
Expert Systems with Applications: An International Journal
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Decision support systems in medicine are designed to aid healthcare professionals on making clinical decisions. Clinical Algorithms derived from Clinical Practice Guidelines (CPGs) make explicit the knowledge necessary to assist physicians in order to make appropriate decisions. Decision support systems for healthcare procedures are supposed to answer questions about what to do and with what time restrictions. Unfortunately, so far we are not able to answer the second question, as clinical algorithms do not contain temporal constraints. Here, our objective is to produce explicit knowledge on temporal restrictions for healthcare procedures. This is reached by generating temporal models from hospital databases. First, we have identified macro-temporality as a constraint on the time required to evolve one step in a clinical algorithm. We have decided to use Timed Transition Diagrams (TTDs) as a structure to represent clinical algorithms, extended with macro-temporality constraints. Then we have identified three different data levels in hospital databases and we have proposed an algorithm to generate macro-temporality in TTDs for each data level.