There is a pressing need in clinical practice to mitigate (identify and address) adverse interactions that occur when a comorbid patient is managed according to multiple concurrently applied disease-specific clinical practice guidelines (CPGs). In our previous work we described an automatic algorithm for mitigating pairs of CPGs. The algorithm constructs logical models of processed CPGs and employs constraint logic programming to solve them. However, the original algorithm was unable to handle two important issues frequently occurring in CPGs - iterative actions forming a cycle and numerical measurements. Dealing with these two issues in practice relies on a physician's knowledge and the manual analysis of CPGs. Yet for guidelines to be considered stand-alone and an easy to use clinical decision support tool this process needs to be automated. In this paper we take an additional step towards building such a tool by extending the original mitigation algorithm to handle cycles and numerical measurements present in CPGs.
|Original language||English (US)|
|Title of host publication||Artificial Intelligence in Medicine - 14th Conference on Artificial Intelligence in Medicine, AIME 2013, Proceedings|
|Number of pages||6|
|State||Published - 2013|
|Event||14th Conference on Artificial Intelligence in Medicine, AIME 2013 - Murcia, Spain|
Duration: May 29 2013 → Jun 1 2013
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||14th Conference on Artificial Intelligence in Medicine, AIME 2013|
|Period||5/29/13 → 6/1/13|
Copyright 2014 Elsevier B.V., All rights reserved.
- Clinical Decision Support Systems
- Computerized Clinical Practice Guidelines
- Constraint Logic Programming