A Hybrid Model for Aiding in Decision Making for the

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A Hybrid Model for Aiding in Decision Making for the Neuropsychological Diagnosis of Alzheimer's Disease Ana Karoline Araújo de Castro, Plácido Rogério Pinheiro, Mirian Caliope Dantas Pinheiro Master Degree in Applied Computer Sciences University of Fortaleza Av. Washington Soares, 1321 - Bloco J sala 30, CEP: 60811-905, Fortaleza, Ceará, Brazil [email protected], {placido,caliope}@unifor.br

Abstract. This work presents a hybrid model, combining Bayesian Networks and the Multicriteria Method, for aiding in decision making for the neuropsychological diagnosis of Alzheimer's disease. Due to the increase in life expectancy there is higher incidence of dementias. Alzheimer's 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.

Key words: Diagnosis, neuropsychological, CERAD, alzheimer's, MACBETH, multicriteria.

1 Introduction The World Health Organization [22] estimates that in 2025 the population over age 65 will be 800 million, with 2/3 in developed countries. It is expected that in some countries, especially in Latin America and Southeast Asia there will be an increase in the elderly population of 300% in the next 30 years. With the increase in life expectancy health problems among the elderly population also increase and these complications tend to be of long duration, requiring qualied personnel, multi-disciplinary teams, and high cost extra exams and equipment. Health care systems will have to confront the challenge of aiding patients and those responsible for them. The costs will be enormous the whole world over.

As the population increases the number of dementias increases as a consequence. There are numerous causes of dementias and specic diagnosis depends on knowledge of dierent clinical manifestations and a specic and obligatory sequence of complementary exams [7]. The initial symptoms of dementia can vary, but the loss of short term memory is usually the main or only characteristic to be brought to the attention of the doctor in the rst appointment. Even so, not all cognitive problems in elderly people are due to dementia. Careful questioning of patients and family members can help to determine the nature of cognitive damage and narrow the choices for diagnosis [21]. Alzheimer's disease (AD) is the most frequent cause of dementia and makes up 50% of the cases in the 65+ age group [8]. The main focus of this work is to develop a multicriteria model for aiding in decision making for the neuropsychological diagnosis of Alzheimer's disease, using Bayesian networks as a modeling tool. The processes of problem denition, qualitative and quantitative modeling, and evaluation are presented here. In this work, the modeling and evaluation processes have been conducted with the aid of a medical expert and bibliographic sources. Batteries of standardized assessments which help in the neuropsychological diagnosis of Alzheimer's disease were used for the application of the model. The battery of tests used in this work is from the Consortium to Establish a Registry for Alzheimer's disease (CERAD). We have sought to discover which questions are most relevant for neuropsychological diagnosis of Alzheimer's disease by using this battery of tests. The work has produced a Bayesian network and a ranking with the classication of these questions. This ranking is composed of the construction of judgment matrixes and constructing value scales for each Fundamental Point of View already dened. The construction of cardinal value scales was implemented through MACBETH.

2 Diagnosis of Alzheimer's Disease Alzheimer's disease is characterized by a progressive and irreversible decline in some mental functions, such as memory, time and space orientation, abstract thinking, learning, the incapacity to carry out simple calculations, language disturbances, communication and the capacity to go about daily activities [21]. Diagnosis of Alzheimer's disease [1,6,7,8,11,13,16,17,19,20,21,22] is based on the observation of compatible clinical symptoms and the exclusion of other causes of dementia by means of laboratory exams and structural neuro-imagery. A variety of clinical instruments are used to come to a diagnosis such as a complete medical history, tests to evaluate memory and mental state, evaluation of the degree of attention and concentration of abilities in solving problems and level of communication.

3 CERAD - An Overview The original mandate of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) in 1986 was to develop a battery of standardized assessments for the evaluation of cases with Alzheimer's disease who were enrolled in NIAsponsored Alzheimer's Disease Centers (ADCs) or in other dementia research programs [15]. Despite the growing interest in clinical investigations of this illness at that time, uniform guidelines were lacking as to diagnostic criteria, testing procedures, and staging of severity. This lack of consistency in diagnosis and classication created confusion in interpreting various research ndings. CERAD was designed to create uniformity in enrollment criteria and methods of assessment in clinical studies of Alzheimer's Disease and to pool information collected from sites joining the Consortium. CERAD developed the following standardized instruments to assess the various manifestations of Alzheimer's disease: Clinical Neuropsychology, Neuropathology, Behavior Rating Scale for Dementia, Family History Interviews and Assessment of Service Needs.

4 Model Construction 4.1 Denition of Problem In studies developed by [9] and [10] the application of the multicriteria model for aiding in diagnosis of Alzheimer's disease was presented. These models were initially validated using two patients and later, with a group of 235 patients who had not yet been diagnosed with Alzheimer's disease. In the validation with the bigger group of people we used other data set that was obtained through of study realized in 2005 with 235 elderly people in the city of São José dos Campos, SP, Brazil. We used in this study a questionnaire with 120 questions that supply demographic-social data, analyze the subjective perception of the elderly, the mental and physical health (aspects cognitive and emotional), independency in day-by-day, in addition to familiar and social support and the use of services. In the present study, we sought to validate the model in a group of patients who had already been diagnosed. So, in this validation we used a neuropsychological battery of assessments which has been applied all over the world. In the next sections the structures of the model are shown. Initially, we dened the mapping of the questions from neuropsychological part of the battery of assessments. As a result of this mapping a Bayesian network was created, based on in the opinion of specialists. After that with the Bayesian network, a multicriteria model was structured that indicated the questions of the biggest decisive impact for the diagnosis. In addition to that, the questions of the biggest impact for the diagnosis were shown to the percentage of elderly people that presented the neuropsychologicals symptoms of Alzheimer's disease.

4.2 Bayesian Network Model The Bayesian network is a graphic model that has an acyclic directed graph. The nodes and arcs of the model represent, respectively, the universal variables U=(A1, A2,...,An) and the dependencies among the variables. In the network that was constructed for the medical problem modeled, the direction of the arcs represents the relations of consequence-cause among the variables. For example, we have an arc between an A node to a B node, we say that an A node represents, semantically, a cause of B and we use the name A as one of the parents of B [12]. There are other works with applications using Bayesian Network in diagnosis of Alzheimer's disease [5]. During the construction of the Bayesian network, we sought to use the bibliographic data with a health professional. For this reason, meetings were held with a specialist nurse that helped in the structure of the network and the subsequent quantication. In obtaining the structural model of the network, sought to identify what information relating to the problem of diagnosis, which were present in the neuropsychological part of the battery of assessment which could be represented as variables of the network, as well as the causal relationships between these variables.

Fig. 1. Network for the diagnosis of Alzheimer's disease in relation to the criteria Mini-Mental State Examination.

In the present model, the nodes of the network can be divided in the following way: 1. The main objective of the study: Denition of the neuropsychological diagnosis of Alzheimer's disease.

2. Create uniformity in enrollment criteria and methods of assessment in neuropsychological studies of Alzheimer's diagnosis. 3. Areas evaluated by the neuropsychological battery of assessments and used in the network construction [15]: Verbal Fluency, Boston Naming, Mini-Mental State Exam, Word List Memory, Constructional Praxis, Word List Recall, Word List Recognition and Constructional Praxis (Recall). The network structure for Mini-Mental State Exam is presented in gure 1. We used Netica Software (http://www.norsys.com) for the construction of the Bayesian network. The use of the network occurs during the denition of the descriptors and in the evaluation of the nal results obtained in the multicriteria model that will be shown in the next section.

4.3 Multicriteria Model According to [2], in decision making it is necessary to look for elements that can answer the questions raised in order to clarify and make recommendations or increase the coherency between the evolution of the process and the objectives and values considered in the environment. In this study we used the Multi-Criteria Decision Analysis (MCDA) that is a way of looking at complex problems that are characterized by any mixture of objectives, to present a coherent overall picture to decision makers. As a set of techniques, MCDA provides dierent ways of measuring the extent to which options achieve objectives. A substantial reading on MCDA methods can be found in [2,3,4,14,18], where the authors address the denitions and the problems that are involved in the decision making process. Although it is not simple, the task of constructing the value tree is greatly facilitated with the aid of the Bayesian network. A great volume of information and inter-relations of the raised concepts are provided through the network. In this study, we used the M-MACBETH for MCDA tool (http://www. m-macbeth.com) to help in the resolution of the problem.

Fig. 2. Problem value tree.

The evaluation process is composed of the construction of judgment matrixes and constructing value scales for each Fundamental point of view (FPV) already dened. The construction of cardinal value scales will be implemented through the MACBETH methodology developed by [4]. Figure 2 shows the tree corresponding to the FPVs. The tree represents the denitive structure of the problem that evaluates the neuropsychological diagnosis of Alzheimer's disease. From the family of FPVs it is possible to carry out the evaluation of the attractiveness of the options for each interest. Although the denition of the describers of impact is a dicult task, it decisively contributes to a good formation of judgments and a just and transparent evaluation [3].

4.4 Describers An FPV is operational in the moment that has a set of levels of associated impacts (describers). These impacts are dened for Nj, that can be ordered in decreasing form according to the describers [18].

Table 1. Describer for the FPV - Mini-Mental State Exam.

In this step of construction of the describers, the decisions were made during the meetings with the health professional involved in the process.

Each FPV was operationalized in such a way as to evaluate the inuence of the questions evaluated in the elderly patients that correspond to each criterion during the denition of the neuropsychological diagnosis of Alzheimer's disease. For the evaluation of each FPV, the possible states were dened. Each FPV has a dierent quantity of states. These states were dened according to the exams or questions involved for each describer. Its important remember that the describers has a structure of complete preorder, otherwise, a superior level is always preferable a least level. For the evaluation of the FPV Mini-Mental State Exam were dened 38 states possible. Table 1 shows the describer of the Mini-Mental State Exam with 16 levels of impact.

4.5 Analysis of Impacts In this step, the analysis of impacts is carried out, according to each FPV: (i) the lowest and highest values of the impacts; and (ii) the relevant aspects of the distribution of the impacts in each one. In this work, for each describer, the same values were considered to get the value function for each FPV. Therefore, scores higher than 60, obtained through the judgments matrixes were considered risk describers during the evaluation of diagnosis, in other words, the elderly person that has a great number of answers considered right in the denition of the diagnosis, becomes part of the group of people with a great probability of developing Alzheimer's disease. This perception was dened by the health professional.

4.6 Evaluation After the denition of the FPVs, family and the construction of the describers, the next step is the construction of the cardinal value scales for each FPV. The evaluations of the judgments matrixes were made according to the opinion of the decision maker, the health area professional.

Fig. 3. Judgment of all the FPVs. After evaluating the alternatives of all the FPVs individually, an evaluation of the FPVs in one matrix only was carried out. For this, a judgment matrix

was created in which the decision maker's orders are dened according to the preference of the decision maker. The decision maker dened the order based on what he judged to be more important in deciding on a diagnosis. Figure 3 presents the judgment matrix of the FPVs.

4.7 Results In this step, we show the nal result of the model, the contribution of the criteria for the neuropsychologicals diagnosis of Alzheimer's disease. We can see the describer values for each criterion. These values show the importance of choosing these questions that are part of the describers during the denition of the diagnosis. Analyzing the FPV1 (Verbal Fluency), two describers achieved a value above that which was dened in the impact analysis. They are describers N2 and N3 with values of 77.78 and 88.89 respectively. In FPV2 (Boston Naming), three describers achieved a value above that which was dened in the impact analysis. They are describers N4, N5 and N6 with values of 82.35, 88.24 and 94.12 respectively. In FPV3 (Mini-Mental State Exam), there were 10 describers which achieved the minimum value in impact analysis. They were describers N7 to N16, with values of 67.41, 78.57, 89.73, 92.41, 95.09, 97.77, 98.21, 98.66, 99.11 and 99.55. In FPV4 (Word List Memory), two describers achieved a value above that which was dened in the impact analysis. They are describers N2 and N3 with values of 77.78 and 88.89 respectively. In FPV5 (Constructional Praxis), four describers achieved a value above that which was dened in the impact analysis. They are describers N5 to N8 with values of 68.42, 84.21, 91.23 and 98.25 respectively. In FPV6 (Word List Recall) only one describer achieved the minimum value. Its value is 87.50. In FPV7 (Word List Recognition), only one describer achieved the minimum value. Its value is 87.50. In FPV8 (Constructional Praxis (Recall)), six describers achieved a value above that which was dened in the impact analysis. They are describers N5 to N10 with values of 61.90, 84.13, 90.48, 93.65, 96.83 and 98.41 respectively. With this result, we can conclude that the questions that are part of these describers should be preferentially applied during the denition of neuropsychological diagnosis of Alzheimer's disease. Another important factor to be analyzed is the great quantity of describers which achieved the minimum value in FPV3. Many studies show the importance of the denition of the diagnosis of dementia that should be carried out before the denition of the diagnosis of Alzheimer's disease [6], because many diseases can be confused with dementias and as a consequence, be confused with Alzheimer's disease. This is merely to underline the importance of these criteria for the solution of this problem.

With these results we can carry out a probabilistic analysis with the objective of producing the proles of the elderly patients that were diagnosed by using this battery of assessments.

5 Conclusion The diagnosis of Alzheimer's disease is made up of many steps. The rst step is to discover if the patient has dementia and then the patient is assessed to see if he has Alzheimer's. Due to these limitations, this study sought to nd the best way possible in the decision making process of dening this diagnosis. By using the neuropsychological part of battery of assessments adopted by CERAD we attempted to select the main questions involved in diagnosis of Alzheimer's. This battery of assessments was chosen because it uses all the steps of the diagnosis process, and has been used all over the world. The MACBETH multicriteria method was used to aid in decision making with the help of the Bayesian network during the mapping of the variables involved in the problem. The criteria were dened according to the neuropsychological CERAD areas of assessment. The questions that make up the battery of assessments were dened as the describers of the problem. With this information, the judgement matrixes were constructed using MACBETH software. After evaluating the matrixes, a ranking was obtained showing all the questions, from most important to least important with respect to the diagnosis of Alzheimer's. With these results we can carry out a probabilistic analysis with the objective of producing the proles of the elderly patients that were diagnosed by this assessment. As a future project, this model can be extended with the inclusion of new criteria or new models which can be developed using other batteries of assessments.

Acknowledgments. The authors thank the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) for the divulgation of the data utilized in this case study. Ana Karoline Araújo de Castro is thankful to FUNCAP for the support she has received for this project.

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