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Abstract computation in schizophrenia detection through artificial neural network based systems

机译:基于神经网络的精神分裂症检测中的抽象计算

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摘要

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.
机译:精神分裂症代表精神不确定性的长期状态,这种状态可能会终结行为,思想和情感之间的关系。也就是说,它可能导致不可靠的感知,不合适的动作和感觉以及精神分裂感。确实,它的诊断要花很长时间。持续的干扰迹象至少持续6(六个)个月。精神病医生一旦被发现,就会通过临床访谈和一系列心理测试做出诊断,主要是为了避免其他精神状态或疾病的诊断。不可否认,识别精神分裂症的主要问题是难以将其症状与与不同精神不振或作用相关的症状区分开。因此,这项工作将集中在基于逻辑编程和人工神经网络方法进行计算的诊断支持系统的知识表示和推理程序方面,利用一种新颖的知识表示和推理方法。推理,旨在解决与处理(即代表和推理)缺陷信息相关的问题。

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