A multi-agent based medical image multi-display visualization system

The evolution of equipments used in the medical imaging practice, from 3-tesla Magnetic Resonance (MR) units and 64-slice Computer Tomography (CT) systems to the latest generation of hybrid Positron Emission Tomography (PET)/CT technologies is fast producing a volume of images that threatens to overload the capacity of the interpreting radiologists. On the other hand multi-agents systems are being used in a wide variety of research and application fields. Our work concerns the development of a multi-agent system that enables a multi-display medical image diagnostic system. The multi-agent system architecture permits the system to grow (scalable) i.e., the number of displays according to the user’s available resources. There are two immediate benefits of this scalable feature: the possibility to use inexpensive hardware to build a cluster system and the real benefit for physicians is that the visualization area increases allowing for easier and faster navigation. In this way an increase in the display area can help a physician analyse and interpret more information in less tim

Web-based medical teaching using a multi-agent system

Web-based teaching via Intelligent Tutoring Systems (ITSs) is considered as one of the most successful enterprises in artificial intelligence. Indeed, there is a long list of ITSs that have been tested on humans and have proven to facilitate learning, among which we may find the well-tested and known tutors of algebra, geometry, and computer languages. These ITSs use a variety of computational paradigms, as production systems, Bayesian networks, schema-templates, theorem proving, and explanatory reasoning. The next generation of ITSs are expected to go one step further by adopting not only more intelligent interfaces but will focus on integration. This article will describe some particularities of a tutoring system that we are developing to simulate conversational dialogue in the area of Medicine, that enables the integration of highly heterogeneous sources of information into a coherent knowledge base, either from the tutor’s point of view or the development of the discipline in itself, i.e. the system’s content is created automatically by the physicians as their daily work goes on. This will encourage students to articulate lengthier answers that exhibit deep reasoning, rather than to deliver straight tips of shallow knowledge. The goal is to take advantage of the normal functioning of the health care units to build on the fly a knowledge base of cases and data for teaching and research purposes

Uma abordagem multi-agente ao ensino médico utilizando a Web

O ensino baseado na Web, utilizando Sistemas Tutoriais Inteligentes (STIs), é considerado um dos mais bem sucedidos empreendimentos da Inteligência Artificial. Na verdade, há uma longa lista de STIs já testados e que demonstraram facilitar o processo de aprendizagem, entre os quais se encontram os que respondem por disciplinas como a Álgebra, Geometria, Línguas e Informática. Estes STIs utilizam uma grande variedade de paradigmas computacionais, tais como Sistemas de Produção, Redes Bayesianas, Esquemas de Templates, Prova de Teoremas, e/ou Raciocínio Baseado em Casos. Espera-se, por conseguinte, que a próxima geração de STIs vá um pouco mais longe, adoptando não só interfaces inteligentes, mas centrando-se na integração de sistemas. Neste artigo iremos abordar algumas das particularidades de um sistema tutorial que se está a desenvolver na área médica, que permite a integração de fontes de informação altamente heterogêneas numa base de conhecimento coerente, tanto do ponto de vista do tutor, como das unidades temáticas em si, ou seja, os conteúdos do sistema são criados de forma dinâmica pelos médicos e restantes profissionais em saúde, no seu labor do dia-a-dia. Isto passa por se aproveitar o normal funcionamento das unidades de saúde para construir, em tempo real, uma base de conhecimento de casos e de dados para fins de investigação e ensino

Case based reasoning versus artificial neural networks in medical diagnosis

Embedding Machine Learning technology into Intelligent Diagnosis Systems adds a new potential to such systems and in particular to the imagiology ones. In our work, this is achieved using the data acquired from MEDsys, a computational environment that supports medical diagnosis systems that use an amalgam of knowledge discovery and data mining techniques, which use the potential of an extension to the language of Logic Programming, with the functionalities of a connectionist approach to problem solving using Artificial Neural Networks. One’s goal aims to conceive an alternative method to detect medical pathologies, as an alternative to the one in use in the actual medical diagnostic system; i.e., Case Based Reasoning versus Artificial Neural Networks. A comparative study of these two approaches to machine learning will be presented, taking into account its applicability in MEDsys

Medical imaging environment : a multi-agent system for a computer clustering based multi-display

This paper presents a solution to minimize a problem that normally arises from the huge amount of images that a radiologist usually has to interpret. A multi-agent system that implements a multi-display for medical imaging based on computer clustering of normal personal computers is therefore described, as well as the multi-agent architecture that caters for the system evolution. An evaluation study was performed and its results are presented

Adverse events and near misses in medical imaging

Em 2000, o relatório do Instituto da Medicina, “To Err Is Human: Building A Safer Health System”, captou a atenção da opinião pública ao relevar a magnitude do problemática do erro médico e da inerente segurança do doente: cerca de 44 000 a 98 000 pessoas morrem por ano nos Estados Unidos da América devido a erros médicos. Actualmente, verifica-se um crescente interesse na gestão do risco na área médica, em particular na gestão dos eventos adversos. Tem sido sobretudo devido ao empenho da Organização Mundial de Saúde que este campo de investigação tem ganho cada vez mais a atenção que merece. A Imagiologia é uma das área de risco com grande potencial para o aparecimento de erros, nomeadamente devido à multiplicidade de técnicas utilizadas, aos diversos intervenientes e à complexidade de todo o circuito que envolve a realização de exames. Muitos dos métodos utilizados para analisar a segurança na prestação de cuidados de saúde foram adaptados de técnicas de gestão de risco em indústrias de alto risco (e.g. indústria química, nuclear e aeronáutica). É reconhecido que é possível apreender mais com os erros do que com êxitos e os sistemas de registo de erros destas industrias têm prestado um valioso contributo para o estudo da prevenção e gestão do erro. No mínimo os sistemas de registo de eventos adversos ajudam a identificar perigos e riscos, fornecendo informações relevantes sobre os aspectos do sistema que devem ser melhorados. Contudo, a acumulação de dados potencialmente relevantes contribui muito pouco para a melhoria do serviço de saúde. Torna-se fundamental aplicar modelos para identificar as causas subjacentes ao sistema, as causas fundamentais dos eventos e potenciar a partilha de experiência e conhecimento. Neste artigo, é sugerida uma solução para reduzir os eventos adversos, através da identificação e eliminação das causas fundamentais que estão na sua origem. Para tal, é descrito o modo como o Modelo de Classificação de Eindhoven foi adaptado e estendido especificamente para a Imagiologia. A abordagem proposta inclui a análise das causas fundamentais e introduz conceitos de informação incompleta através da utilização de operadores lógico-matemáticos formalmente sustentados. Este modelo é a base do sistema de registo e aprendizagem de eventos adversos e não conformidades que foi desenvolvido para a Imagiologia e que se encontra implementado em duas instituições de saúde Portuguesas. Os objectivos, características e modo de funcionamento deste sistema são apresentados ao longo deste artigo.In 2000, the Institute of Medicine’s report, “To Err Is Human: Building a Safer Health System”, has caught the public attention documenting the magnitude of the medical error problem and the inherent patient safety: medical errors cause between 44,000 and 98,000 deaths annually in the United States. Currently, there is a growing interest in risk management on the medical field, particularly in the management of adverse events. It has been mainly due to the commitment of the World Health Organization, that this field of research has gained increasing the attention it deserves. Medical imaging is one of the high-risk fields for the occurrence of errors, especially due to the multiplicity of techniques, the several stakeholders and the complexity of the whole circuit that involves the conduct of studies. Many of the methods used to analyze patient safety were adapted from risk-management techniques in high-risk industries (e.g. chemical, nuclear power and aviation industry). It is recognized that we can learn more from our mistakes than from our successes and the reporting systems in these industries have provided a valuable contribution to error's prevention and risk management techniques. At a minimum, adverse events reporting systems can help to identify hazards and risks, providing important information on the system aspects that should be improved. However, the accumulation of potentially relevant data contributes little to healthcare services improvement. It is crucial to apply models to identify the underlying system failures, the root causes, and enhance the sharing of knowledge and experience. In this paper, it is suggested a solution to reduce adverse events, by identifying and eliminating the root causes that are in their source. How the Eindhoven Classification Model was adapted and extended specifically for the Medical Imaging field is also presented. The proposed approach includes the root causes analysis and introduces incomplete information concepts through the use of logical-mathematical operators formally sustained. This model is the basis of the adverse event and near misses reporting and learning system that was developed for Medical Imaging and is implemented in two Portuguese healthcare institutions. The objectives, characteristics and function of this system are presented throughout this article

A logic programming approach to medical errors in imaging

Background: In 2000, the Institute of Medicine reported alarming data on the scope and impact of medical errors calling the public attention. One solution to this problem is the adoption of adverse event reporting and learning systems that can help to identify hazards and risks. The accumulation of potentially relevant data in databases contributes little to quality improvement. It is crucial to apply models to identify the adverse events root causes, enhance the sharing of knowledge and experience. The efficiency of the efforts to improve patient safety has been frustratingly slow. Some of this insufficient of progress may be assigned to the lack of systems that take into account the characteristic of the information about the real world. On our daily life, we make most of our decisions, if not all of them, based on incomplete, uncertain and even forbidden or contradictory information. Knowledge is central to the problems of modern economy and society. One’s knowledge is less based on exact facts and more on hypothesis, perceptions or indications. Purpose: From the data collected on our adverse event reporting and learning system, and through Extended Logic Programming and Knowledge Representation, we intend to generate reports that identify the most relevant causes and define improvement strategies, concluding about the impact, place of occurrence, type of form and type of event recorded in the healthcare institutions. Results and Conclusions: The Eindhoven Classification Model was extended and adapted to the medical imaging field and used to classify adverse events root causes. Extended Logic Programming was used for knowledge representation with defective information, allowing for the modelling of the universe of discourse in terms of default data and knowledge. A systematization of the evolution of the body of knowledge about Quality of Information embedded in the Root Cause Analysis was accomplished. An adverse event reporting and learning system was developed based on the presented approach to medical errors in imaging. This system was deployed in two Portuguese healthcare institutions presenting useful results. The system enabled to verify that the majority of occurrences were concentrate in a few events that could be avoided. The developed system allowed automatic knowledge extraction, enabling report generation with strategies for quality improvement

E-learning in medical environments using intelligent tutoring systems

Comunicação apresentada na International Conference on Knowledge Engineering and Decision Support, 1, Porto, 2004.e-Learning in the health care area is becoming extremely popular. e-Learning implementations using holding portals has shown advantages in universities enabling them to implement expansion processes at reduced costs. Experts can launch new learning courses or even simple instructions for there students. Increase of productivity, efficiency and quality of service are the main challenges. Education and training in this context are strategic objectives. Simulation also plays a fundamental role in e-learning and information technology management. Simulation procedures should consequently be integrated in the portal’s functionalities. In this work we present an electronic system that enables the integration of highly heterogenic information into a coherent medical knowledge base in the context of an e-learning system oriented for the health care area

A logic programming based adverse event reporting and learning system

Changes are taking place in the way patients, physicians, administrators, legislators and society in general view healthcare, including its quality and safety. The conclusion that more people may die as a result of medical errors than from injuries sustained in motor vehicle accidents is alarming. An adverse event reporting system may help to improve patient safety and the quality of the healthcare institution. However, the accumulation of potentially relevant data in databases contributes little to healthcare services improvement. It is crucial to apply models to identify the underlying system failures, the root causes that led to the event and enhance the sharing of knowledge and experience. In the real world complete information is hard to obtain, so systems should have the ability to reason with incomplete information. We developed a model to classify the adverse events root causes in the medical imaging field where our logic programming approach allows the representation of incomplete information. In this paper we present a model for the adverse events root causes classification in the medical imaging field and an adverse event reporting and learning system that applies the developed model. This system is deployed in two Portuguese healthcare institutions with promising results. The conceptualized logic model offered the means for knowledge extraction, providing the identification of the most significant causes and suggestions of changes in the healthcare organization policies and procedures.(undefined