By Isabelle Bichindaritz, Sachin Vaidya, Ashlesha Jain
This publication is a continuation of the volumes supplying numerous per-spectives on computational intelligence in healthcare [1-3]. This publication is aimed to supply a pattern of the country of artwork within the prac-tical purposes of computational intelligence paradigms in health-care. It comprises nineteen chapters on utilizing a number of computational clever paradigms in healthcare resembling clever brokers and case-based reasoning. a couple of purposes and case reviews are awarded. This publication is focused in the direction of scientists, software engineers, pro-fessors, health and wellbeing execs, professors and students.
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Extra info for Computational Intelligence in Healthcare 4: Advanced Methodologies
This computerised enactment of CGs brings several economical and medical benefits for practitioners and patients, easing, automating and standardising healthcare treatments. The agent-based platform mimics real relationships and roles of real healthcare organisations, but the management is quite different. The system permits a balance of the load (avoiding or mitigating waiting lists). , availability 24/7). An important feature of medical systems is the knowledge representation. HeCaSe2 uses different ontologies that separate the knowledge from its use, and permits to guide the agents’ execution with this ontology-based representation.
The use of agent technology, as had been previously argued in [11, 39], certainly seems an interesting option to consider in this kind of distributed systems. K4Care agents model their human counterparts in a realistic and natural way, mimicking their real HC behaviours modelled in the knowledge structures. Agents can be dynamically deployed and managed into the system creating, for example, an Actor Agent associated to a new patient who will immediately allow and manage the interaction of the new user with the system in a personalized manner.
As will be argued in section 3, all those possibilities make intelligent agents and multi-agent systems an ideal paradigm to model or simulate healthcare processes. Healthcare entities can be modelled as agents, implementing appropriate behaviours and developing high-level negotiation and communication processes, which represent faithfully real-world medical interactions. The use of intelligent agents in Medicine may be considered a complementary technique to improve the performance of typically ad-hoc and closed medical systems, in terms of interoperability, scalability and flexibility .
Computational Intelligence in Healthcare 4: Advanced Methodologies by Isabelle Bichindaritz, Sachin Vaidya, Ashlesha Jain