Towards the Applied Hybrid Model in Decision Making: A Neuropsychological Diagnosis of Alzheimer's Disease Study Case

Castro, Ana Karoline Araújo de; Pinheiro, Plácido Rogério; Pinheiro, Mirian Calí­ope Dantas; Tamanini, Isabelle

doi:10.1080/18756891.2011.9727766

Cited by 23 publications

(11 citation statements)

References 31 publications

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“…Many smart techniques were incorporated into the decision-making process. These methods are based on technologies that use concepts of artificial intelligence (AI), such as systems expert's genetic algorithms [ 26 , 27 ], neural networks [ 28 ], intelligent agents, reasoning based on case studies [ 2 , 29 ], and fuzzy logic [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

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Handling Diagnosis of Schizophrenia by a Hybrid Method

Nunes

Pinheiro

Cavalcante

et al. 2015

Computational and Mathematical Methods in Medicine

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Psychotics disorders, most commonly known as schizophrenia, have incapacitated professionals in different sectors of activities. Those disorders have caused damage in a microlevel to the individual and his/her family and in a macrolevel to the economic and production system of the country. The lack of early and sometimes very late diagnosis has provided reactive measures, when the professional is already showing psychological signs of incapacity to work. This study aims to help the early diagnosis of psychotics' disorders with a hybrid proposal of an expert system that is integrated to structured methodologies in decision support (multicriteria decision analysis: MCDA) and knowledge structured representations into production rules and probabilities (artificial intelligence: AI).

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Section: Discussionmentioning

confidence: 99%

“…Aiming at the propose of a system for early diagnosis of psychotic disorders, a hybrid model is presented by combining multicriteria methodology for decision support and expert system. Importantly, hybrid models have been used to support decision-making and finding diagnostics for diseases [ 2 – 5 ].…”

Section: Introductionmentioning

confidence: 99%

Handling Diagnosis of Schizophrenia by a Hybrid Method

Nunes

Pinheiro

Cavalcante

et al. 2015

Computational and Mathematical Methods in Medicine

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“…De Castro et al [22] have conducted research on the diagnosis of Alzheinmer's disease using a hybrid model of influence diagrams and multicriteria decision making methods. An influence diagram is a generalization of a Bayesian network.…”

Section: Integration Of Computational Intelligence Techniquesmentioning

confidence: 99%

Evaluating Timeliness and Accuracy Trade-offs of Supervised Machine Learning for Adapting Enterprise DRE Systems in Dynamic Environments

Hoffert¹,

Schmidt²,

Gokhale³

2011

IJCIS

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Several adaptation approaches have been devised to ensure end-to-end quality-of-service (QoS) for enterprise distributed systems in dynamic operating environments. Not all approaches are applicable, however, for the stringent accuracy, timeliness, and development complexity requirements of distributed real-time and embedded (DRE) systems. This paper empirically evaluates constant-time supervised machine learning techniques, such as artificial neural networks (ANNs) and support vector machines (SVMs), and presents a composite metric to support quantitative evaluation of accuracy and timeliness for these adaptation approaches.

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“…Influence diagrams (IDs; Howard & Matheson, ) are one of the most common graphical and mathematical decision models used for reasoning under uncertainty. Because IDs represent and model the relationships between decisions, uncertainties, and preferences of decision makers, and they can be evaluated to reveal optimal strategies for decision‐making situations, IDs have become an important area for research and have been widely used in various applications, such as risk analysis (H. Liu, Zhang, & Wan, ), disease diagnosis (De Castro, Pinheiro, Pinheiro, & Tamanini, ), multi‐criteria decision‐making (Sedki & Delcroix, ), and recommender systems (Antonio, Manuel, & Tomás, ).…”

Section: Introductionmentioning

confidence: 99%

Decision‐making under uncertainty through extending influence diagrams with interval‐valued parameters

2018

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Influence diagrams (IDs) are one of the most commonly used graphical and mathematical decision models for reasoning under uncertainty. In conventional IDs, both probabilities representing beliefs and utilities representing preferences of decision makers are precise point‐valued parameters. However, it is usually difficult or even impossible to directly provide such parameters. In this paper, we extend conventional IDs to allow IDs with interval‐valued parameters (IIDs) and develop a counterpart method of Copper's evaluation method to evaluate IIDs. IIDs avoid the difficulties attached to the specification of precise parameters and provide the capability to model decision‐making processes in a situation that the precise parameters cannot be specified. The counterpart method to Copper's evaluation method reduces the evaluation of IIDs into inference problems of Bayesian networks with interval‐valued probabilities. An algorithm based on the approximate inference of Bayesian networks with interval‐valued probabilities is proposed, and extensive experiments are conducted. The experimental results indicate that the proposed algorithm can find the optimal strategies effectively in IIDs, and the interval‐valued expected utilities obtained by proposed algorithm are contained in those obtained by exact evaluating algorithms. The newly development approach would significantly extend the range of IDs for managerial decision support applications where parameters of variables can be specifically defined by only estimated intervals.

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Towards the Applied Hybrid Model in Decision Making: A Neuropsychological Diagnosis of Alzheimer's Disease Study Case

Cited by 23 publications

References 31 publications

Handling Diagnosis of Schizophrenia by a Hybrid Method

Handling Diagnosis of Schizophrenia by a Hybrid Method

Evaluating Timeliness and Accuracy Trade-offs of Supervised Machine Learning for Adapting Enterprise DRE Systems in Dynamic Environments

Decision‐making under uncertainty through extending influence diagrams with interval‐valued parameters

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