Agent-Based Medical Diagnosis Systems

Medical diagnostics elaboration many times is a distributed and cooperative work, which involves more medical human specialists and different medical systems. Recent results described in the literature prove that medical diagnosis problems can be solved efficiently by large-scale medical multi-agent systems. Cooperative diagnosing of medical diagnosis problems by large-scale multi-agent systems makes the diagnoses elaborations easier and may increase the accuracy of elaborated diagnostics. The purpose of the study described in this paper consists in the development of a novel large-scale hybrid medical diagnosis system called LMDS. The LMDS system is composed from physicians, medical expert system agents developed in our previous works and medical ICMA agents. Medical ICMA agents represent a novel class of agents with the ICMA architecture developed in our previous works, endowed with medical diagnosis capability. The main novelty of the LMDS system consists in the novel classes of agent members of the system and the manner in which the members of the system contribute to the problems solving. Each diagnostics can be elaborated cooperatively by more members of the system. The diagnosis system can solve difficult medical diagnosis problems whose solving must be discovered cooperatively by the members of the system. Many difficult medical problem solving requires medical knowledge that cannot be detained by a single physician or a medical computational system. Simulations prove the correctness in operation of the LMDS system

Intelligent Automated Negotiation for Medical Image Segmentation Failure using Multi-Agent Systems

International audienceImage segmentation errors can be fatal in the medical field. Sometimes even automated segmentation methods fail, they can be affected by poor image quality, artifacts or even unexpected noises. A practical task such as the segmentation of medical images is highly required for decision-making either for diagnosis or for the treatment of the patient. In this paper, we present a method based on negotiation strategies, of multi-agent systems, for the detection and correction of segmentation failures. The main advantages of our method are: 1) support for a fast negotiation strategy on a 2D view of each slice of the 3D image; 2) our approach is independent of the initial segmentation method; 3) The method is applicable to a variety of medical structures

Agent-based hybrid framework for decision making on complex problems

Electronic commerce and the Internet have created demand for automated systems that can make complex decisions utilizing information from multiple sources. Because the information is uncertain, dynamic, distributed, and heterogeneous in nature, these systems require a great diversity of intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, in complex decision making, many different components or sub-tasks are involved, each of which requires different types of processing. Thus multiple such techniques are required resulting in systems called hybrid intelligent systems. That is, hybrid solutions are crucial for complex problem solving and decision making. There is a growing demand for these systems in many areas including financial investment planning, engineering design, medical diagnosis, and cognitive simulation. However, the design and development of these systems is difficult because they have a large number of parts or components that have many interactions. From a multi-agent perspective, agents in multi-agent systems (MAS) are autonomous and can engage in flexible, high-level interactions. MASs are good at complex, dynamic interactions. Thus a multi-agent perspective is suitable for modeling, design, and construction of hybrid intelligent systems. The aim of this thesis is to develop an agent-based framework for constructing hybrid intelligent systems which are mainly used for complex problem solving and decision making. Existing software development techniques (typically, object-oriented) are inadequate for modeling agent-based hybrid intelligent systems. There is a fundamental mismatch between the concepts used by object-oriented developers and the agent-oriented view. Although there are some agent-oriented methodologies such as the Gaia methodology, there is still no specifically tailored methodology available for analyzing and designing agent-based hybrid intelligent systems. To this end, a methodology is proposed, which is specifically tailored to the analysis and design of agent-based hybrid intelligent systems. The methodology consists of six models - role model, interaction model, agent model, skill model, knowledge model, and organizational model. This methodology differs from other agent-oriented methodologies in its skill and knowledge models. As good decisions and problem solutions are mainly based on adequate information, rich knowledge, and appropriate skills to use knowledge and information, these two models are of paramount importance in modeling complex problem solving and decision making. Follow the methodology, an agent-based framework for hybrid intelligent system construction used in complex problem solving and decision making was developed. The framework has several crucial characteristics that differentiate this research from others. Four important issues relating to the framework are also investigated. These cover the building of an ontology for financial investment, matchmaking in middle agents, reasoning in problem solving and decision making, and decision aggregation in MASs. The thesis demonstrates how to build a domain-specific ontology and how to access it in a MAS by building a financial ontology. It is argued that the practical performance of service provider agents has a significant impact on the matchmaking outcomes of middle agents. It is proposed to consider service provider agents\u27 track records in matchmaking. A way to provide initial values for the track records of service provider agents is also suggested. The concept of ‘reasoning with multimedia information’ is introduced, and reasoning with still image information using symbolic projection theory is proposed. How to choose suitable aggregation operations is demonstrated through financial investment application and three approaches are proposed - the stationary agent approach, the token-passing approach, and the mobile agent approach to implementing decision aggregation in MASs. Based on the framework, a prototype was built and applied to financial investment planning. This prototype consists of one serving agent, one interface agent, one decision aggregation agent, one planning agent, four decision making agents, and five service provider agents. Experiments were conducted on the prototype. The experimental results show the framework is flexible, robust, and fully workable. All agents derived from the methodology exhibit their behaviors correctly as specified

Using MAS to detect retinal blood vessels

The segmentation of retinal vasculature by color fundus images analysis is crucial for several medical diagnostic systems, such as the diabetic retinopathy early diagnosis. Several interesting approaches have been done in this field but the obtained results need to be improved. We propose therefore a new approach based on an organization of agents. This multi-agent approach is preceded by a preprocessing phase in which the fundamental filter is an improved version of the Kirsch derivative. This first phase allows the construction of an environment where the agents are situated and interact. Then, edges detection emerged from agents’ interaction. With this study, competitive results as compared with those present in the literature were achieved and it seems that a very efficient system for the diabetic retinopathy diagnosis could be built using MAS mechanisms.Fundação para a Ciência e a Tecnologia (FCT

Use of data mining to predict human diseases

In this project we intend to make an intelligent agent that asks the user about their medical symptoms and tries to predict the most probable diseases medical conditions that they might be suffering from Based on the results it can also direct the user patient to go to pharmacy or consult a doctor or to go for medical emergency services It is truly said that Prevention Is Better Than Cure Sometimes diseases like cancers have very minor symptoms in the early stages but if detected this could save a patient s life There is no harm in taking preventive medical advice than regretting later Artificial Neural Networks ANN is currently a hot research area in medicine and it is believed that they will receive extensive application to biomedical systems in the next few years An application called the Instant Physician trained an auto associative memory neural network to store a large number of medical records After training the net can be presented with input consisting of a set of symptoms it will then find the full stored pattern that represents the best diagnosis and treatment This product can be useful for various users such as 1 General Population Patients a This can act as a preliminary advice mechanism for patients before they consult a doctor b They can get suggestions as to whether they need to consult a doctor or a visit to the local pharmacy would be fine for them 2 Medical Professionals a To speed up the process of diagnosis and to reduce human errors involved in finding the possible ailments 3 Medical Undergraduate Students a To understand the common diseases and the symptoms related to them b To understand all possible medical conditions which could be present in the patient who is exhibiting a said symptom 4 Hospitals a Based on the diagnosis hospital websites can display their specialist doctors that the patients can visi