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الگو محور، غیر شناختی، مبتنی بر هوش مصنوعی و یادگیری ماشینی
Explanation: Features of a non-Knowledge-Based CDSS
CDSS’s that do not use a knowledge base use a form of artificial intelligence called machine learning, which allow computers to learn from past experiences and/or find patterns in clinical data. Two types of non-knowledge-based systems are artificial neural networks and genetic algorithms.
Artificial neural networks use nodes and weighted connections between them to analyze the patterns found in the patient data to derive the associations between the symptoms and a diagnosis. This eliminates the need for writing rules and for expert input. However since the system cannot explain the reason it uses the data the way it does, most clinicians don’t use them for reliability and accountability reasons.
Genetic Algorithms are based on simplified evolutionary processes using directed selection to achieve optimal CDSS results. The selection algorithms evaluate components of random sets of solutions to a problem. The solutions that come out on top are then recombined and mutated and run through the process again. This happens over and over till the proper solution is discovered. They are the same as neural networks in that they derive their knowledge from patient data. Non-knowledge-based networks often focus on a narrow list of symptoms like ones for a single disease as opposed to the knowledge based approach which cover many different diseases to diagnosis
Ali Ramezani Malaysia Works in field Native speaker of: Farsi (Persian), Persian (Farsi) PRO pts in category: 4
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Asker: jack pot!!!!!!!
i am translating this page of wikipedia right now!!!!!!!!!
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