Mirian Calíope Dantas Pinheiro

A Multicriteria Model Applied in the Diagnosis of Alzheimer's Disease: A Bayesian Network

This study considers the construction of a multicriteria model to assist in the diagnosis of Alzheimerpsilas disease. Alzheimerpsilas disease is considered the most frequent of the dementias and it is responsible for about 50% of the cases. Due to this fact and the therapeutical limitations in the most advanced stage of the disease, diagnosis of Alzheimerpsilas disease is extremely important and it can provide better life conditions to patients and their families. The main focus of this work is to develop a multicriteria model for aiding in decision making for the diagnosis of Alzheimerpsilas disease, using Bayesian networks as a modeling tool. In this work, the modeling and evaluation processes have been conducted with the aid of a medical expert, bibliographic sources and battery of standardized assessments. The construction of cardinal value scales was implemented through Hiview and Macbeth.

An Approach for the Neuropsychological Diagnosis of Alzheimer’s Disease: A Hybrid Model in Decision Making

This work presents a hybrid model, combining Influence Diagrams and the Multicriteria Method, for aiding to discover, from a battery of tests, which are the most attractive questions, in relation to the stages of Clinical Dementia Rating in decision making for the diagnosis of Alzheimers disease. This disease is the most common dementia. Because of this and due to limitations in treatment at late stages of the disease early diagnosis is fundamental because it improves quality of life for patients and their families. Influence Diagram is implemented using GeNie tool. Next, the judgment matrixes are constructed to obtain cardinal value scales which are implemented through MACBETH Multicriteria Methodology. The modeling and evaluation processes were carried out through a battery of standardized assessments for the evaluation of cases with Alzheimers disease developed by Consortium to Establish a Registry for Alzheimers disease (CERAD).

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

A hybrid model, combining influence diagrams and a multicriteria method, is presented in order to assist with the decision making process about which questions would be more attractive to the definition of the diagnosis of Alzheimers disease, considering the stages of Clinical Dementia Rating. The modeling and evaluation processes were carried out through a battery of standardized assessments for the evaluation of cases with Alzheimers disease developed by Consortium to Establish a Registry for Alzheimers disease.

Applying a decision making model in the early diagnosis of Alzheimer's disease

This study considers the construction of a multicriteria model to assist in the early diagnosis of Alzheimers disease. Alzheimers disease is considered the most frequent of the dementias and it is responsible for about 50% of the cases. Due to this fact and the therapeutical limitations in the most advanced stage of the disease, early diagnosis of Alzheimers disease is extremely important and it can provide better life conditions to patients and their families.

A Hybrid Model for Aiding in Decision Making for the Neuropsychological Diagnosis of Alzheimer's Disease

This work presents a hybrid model, combining Bayesian Networks and the Multicriteria Method, for aiding in decision making for the neuropsychological diagnosis of Alzheimers disease. Due to the increase in life expectancy there is higher incidence of dementias. Alzheimers disease is the most common dementia (alone or together with other dementias), accounting for 50% of the cases. Because of this and due to limitations in treatment at late stages of the disease early neuropsychological diagnosis is fundamental because it improves quality of life for patients and theirs families. Bayesian Networks are implemented using NETICA tool. Next, the judgment matrixes are constructed to obtain cardinal value scales which are implemented through MACBETH Multicriteria Methodology. The modeling and evaluation processes were carried out with the aid of a health specialist, bibliographic data and through of neuropsychological battery of standardized assessments.

A multicriteria model applied in the diagnosis of Alzheimer's disease

This study considers the construction of a multicriteria model to assist in the diagnosis of Alzheimerpsilas disease. Alzheimerpsilas disease is considered the most frequent of the dementias and it is responsible for about 50% of the cases. Due to this fact and the therapeutical limitations in the most advanced stage of the disease, diagnosis of Alzheimerpsilas disease is extremely important and it can provide better life conditions to patients and their families. The main focus of this work is to develop a multicriteria model for aiding in decision making for the diagnosis of Alzheimerpsilas disease, using Bayesian networks as a modeling tool. In this work, the modeling and evaluation processes have been conducted with the aid of a medical expert, bibliographic sources and battery of standardized assessments. The construction of cardinal value scales was implemented through Hiview and Macbeth.

The choice of neuropathological questionnaires to diagnose the Alzheimer's disease based on verbal decision analysis methods

There is a great challenge in identifying the early stages of the Alzheimers disease, which has become the most frequent cause of dementia in the last few years. The aim of this work is to determine which tests, from a battery of tests, are relevant and would detect faster whether the patient is having a normal aging or developing the disease. This will be made applying the method ORCLASS and the Aranau Tool, a decision support system mainly structured on the ZAPROS method. The modeling and evaluation processes were conducted based on bibliographic sources and on the information given by a medical expert.

Towards the Neuropsychological Diagnosis of Alzheimer’s Disease: A Hybrid Model in Decision Making

Dementias are syndromes described by a decline in memory and other neuropsychological changes especially occurring in the elderly and increasing exponentially in function of age. Due to this fact and the therapeutical limitations in the most advanced stage of the disease, diagnosis of Alzheimer’s disease is extremely important and it can provide better life conditions to patients and their families. This work presents a hybrid model, combining Influence Diagrams and the Multicriteria Method, for aiding to discover, from a battery of tests, which are the most attractive questions, in relation to the stages of CDR (Clinical Dementia Rating) in decision making for the diagnosis of Alzheimer’s disease. This disease is the most common dementia. Influence Diagram is implemented using GeNie tool. Next, the judgment matrixes are constructed to obtain cardinal value scales which are implemented through MACBETH Multicriteria Methodology. The modeling and evaluation processes were carried out through a battery of standardized assessments for the evaluation of cases with Alzheimer’s disease developed by Consortium to Establish a Registry for Alzheimer’s disease (CERAD).

Towards the applied hybrid model in decision making: support the early diagnosis of type 2 diabetes

A hybrid model, combining Bayesian network and a multicriteria method, is presented in order to assist with the decision making process about which questions would be more attractive to the definition of the diagnosis of Diabetes type 2. We have proposed the application of an expert system structured in probability rules and structured representations of knowledge in production rules and probabilities (Artificial Intelligence - AI). The importance of the early diagnosis associated with the appropriate treatment is to decrease the chance of developing the complications of diabetes, reducing the impact on our society. Diabetes is a group of metabolic diseases characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both.

Support Tool in the Diagnosis of Major Depressive Disorder

Major Depressive Disorder have been responsible for millions of professionals temporary removal, and even permanent, from diverse fields of activities around the world, generating damage to social, financial, productive systems and social security, and especially damage to the image of the individual and his family that these disorders produce in individuals who are patients, characteristics that make them stigmatized and discriminated into their society, making difficult their return to the production system. The lack of early diagnosis has provided reactive and late measures, only when the professional suffering psychological disorder is already showing signs of incapacity for working and social relationships. This article aims to assist in the decision making to establish early diagnosis of these types of psychological disorders. It presents a proposal for a hybrid model composed of expert system structured methodologies for decision support (Multi-Criteria Decision Analysis - MCDA) and representations of knowledge structured in logical rules of production and probabilities (Artificial Intelligence - AI).