AILGNov 16, 2013

A hybrid decision support system : application on healthcare

arXiv:1311.4086v18 citations
Originality Synthesis-oriented
AI Analysis

This work addresses decision support for healthcare professionals, but it appears incremental as it combines existing methods without claiming major breakthroughs.

The authors tackled the limitations of rule-based expert systems in healthcare decision support, which struggle with learning, evolution, and handling multiple criteria, by proposing a hybrid approach using multi-criteria decision-making guided by case-based reasoning.

Many systems based on knowledge, especially expert systems for medical decision support have been developed. Only systems are based on production rules, and cannot learn and evolve only by updating them. In addition, taking into account several criteria induces an exorbitant number of rules to be injected into the system. It becomes difficult to translate medical knowledge or a support decision as a simple rule. Moreover, reasoning based on generic cases became classic and can even reduce the range of possible solutions. To remedy that, we propose an approach based on using a multi-criteria decision guided by a case-based reasoning (CBR) approach.

Foundations

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