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用于识别阿尔茨海默病患者情感方面的本体模型

Ontological Model in the Identification of Emotional Aspects in Alzheimer Patients.

作者信息

Castillo Salazar David Ricardo, Lanzarini Laura, Gómez Héctor, Thirumuruganandham Saravana Prakash, Castillo Salazar Dario Xavier

机构信息

Centro de Investigación de Ciencias Humanas y de la Educación (CICHE), Universidad Indoamérica, Ambato 180103, Ecuador.

Facultad de Informática, Universidad Nacional de la Plata, La Plata 1900, Argentina.

出版信息

Healthcare (Basel). 2023 May 11;11(10):1392. doi: 10.3390/healthcare11101392.

Abstract

The present work describes the development of a conceptual representation model of the domain of the theory of formal grammars and abstract machines through ontological modeling. The main goal is to develop an ontology capable of deriving new knowledge about the mood of an Alzheimer's patient in the categories of wandering, nervous, depressed, disoriented or bored. The patients are from elderly care centers in Ambato Canton-Ecuador. The population consists of 147 individuals of both sexes, diagnosed with Alzheimer's disease, with ages ranging from 75 to 89 years. The methods used are the taxonomic levels, the semantic categories and the ontological primitives. All these aspects allow the computational generation of an ontological structure, in addition to the use of the proprietary tool Pellet Reasoner as well as Apache NetBeans from Java for process completion. As a result, an ontological model is generated using its instances and Pellet Reasoner to identify the expected effect. It is noted that the ontologies come from the artificial intelligence domain. In this case, they are represented by aspects of real-world context that relate to common vocabularies for humans and applications working in a domain or area of interest.

摘要

本研究通过本体建模描述了形式语法理论和抽象机器领域概念表示模型的发展。主要目标是开发一种本体,能够在徘徊、紧张、抑郁、迷失方向或无聊等类别中推导有关阿尔茨海默病患者情绪的新知识。这些患者来自厄瓜多尔安巴托县的老年护理中心。研究对象包括147名男女患者,均被诊断患有阿尔茨海默病,年龄在75至89岁之间。所使用的方法包括分类层次、语义类别和本体原语。所有这些方面除了使用专有工具Pellet推理器以及Java的Apache NetBeans来完成流程外,还允许通过计算生成本体结构。结果,使用其实例和Pellet推理器生成了一个本体模型,以识别预期效果。需要注意的是,这些本体来自人工智能领域。在这种情况下,它们由与人类通用词汇以及在感兴趣的领域或区域中工作的应用程序相关的现实世界上下文方面来表示。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1903/10218486/09641f6ef1ff/healthcare-11-01392-g001.jpg

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