Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals

Cataloged from PDF version of article.A new classification algorithm, called VFI5 (for Voting Feature Intervals), is developed and applied to problem of differential diagnosis of erythemato-squamous diseases. The domain contains records of patients with known diagnosis. Given a training set of such records, the VFI5 classifier learns how to differentiate a new case in the domain. VFI5 represents a concept in the form of feature intervals on each feature dimension separately. classification in the VFI5 algorithm is based on a real-valued voting. Each feature equally participates in the voting process and the class that receives the maximum amount of votes is declared to be the predicted class. The performance of the VFI5 classifier is evaluated empirically in terms of classification accuracy and running time. (C) 1998 Elsevier Science B.V. All rights reserved

Representing medical decision making strategies in a CBR system

Paper presented at the Sixth German Workshop on Case-Based Reasoning: Foundations, Systems, and Applications, Rostock, Germany.This paper describes and compares the development of two organizational structures to represent medical decision making strategies. We generate the solution to a new problem by applying a previous solution from a medical record in a CBR system that performs decision-making about hypertension drug therapy. The case libraries are structured in accordance with the approaches of flat memory and discrimination network. Cases are originated by a retrospective knowledge acquisition about 47 patients who underwent ambulatory care of a university hospital. The similarity-based retrieval employed in the flat structure resembles what physicians do when handling their routine cases of arterial hypertension. Physicians identify a similar case in memory by recognizing the content embedded in the new situation, like a script. The hypothetico-deductive method for searching the case solution follows a similar strategy to the one represented in the prioritized discrimination network. The inclusion of cases in the case library of the discrimination network required more complex procedures than in the case library of the flat memory. These two decision support systems could contribute significantly to patient care. The system we are researching on has educational purposes as well

Contributions to artificial intelligence: the IIIA perspective

La intel·ligència artificial (IA) és un camp científic i tecnològic relativament nou dedicat a l'estudi de la intel·ligència mitjançant l'ús d'ordinadors com a eines per produir comportament intel·ligent. Inicialment, l'objectiu era essencialment científic: assolir una millor comprensió de la intel·ligència humana. Aquest objectiu ha estat, i encara és, el dels investigadors en ciència cognitiva. Dissortadament, aquest fascinant però ambiciós objectiu és encara molt lluny de ser assolit i ni tan sols podem dir que ens hi haguem acostat significativament. Afortunadament, però, la IA també persegueix un objectiu més aplicat: construir sistemes que ens resultin útils encara que la intel·ligència artificial de què estiguin dotats no tingui res a veure amb la intel·ligència humana i, per tant, aquests sistemes no ens proporcionarien necessàriament informació útil sobre la naturalesa de la intel·ligència humana. Aquest objectiu, que s'emmarca més aviat dins de l'àmbit de l'enginyeria, és actualment el que predomina entre els investigadors en IA i ja ha donat resultats impresionants, tan teòrics com aplicats, en moltíssims dominis d'aplicació. A més, avui dia, els productes i les aplicacions al voltant de la IA representen un mercat anual de desenes de milers de milions de dòlars. Aquest article resumeix les principals contribucions a la IA fetes pels investigadors de l'Institut d'Investigació en Intel·ligència Artificial del Consell Superior d'Investigacions Científiques durant els darrers cinc anys.Artificial intelligence is a relatively new scientific and technological field which studies the nature of intelligence by using computers to produce intelligent behaviour. Initially, the main goal was a purely scientific one, understanding human intelligence, and this remains the aim of cognitive scientists. Unfortunately, such an ambitious and fascinating goal is not only far from being achieved but has yet to be satisfactorily approached. Fortunately, however, artificial intelligence also has an engineering goal: building systems that are useful to people even if the intelligence of such systems has no relation whatsoever with human intelligence, and therefore being able to build them does not necessarily provide any insight into the nature of human intelligence. This engineering goal has become the predominant one among artificial intelligence researchers and has produced impressive results, ranging from knowledge-based systems to autonomous robots, that have been applied to many different domains. Furthermore, artificial intelligence products and services today represent an annual market of tens of billions of dollars worldwide. This article summarizes the main contributions to the field of artificial intelligence made at the IIIA-CSIC (Artificial Intelligence Research Institute of the Spanish Scientific Research Council) over the last five years

Case-based medical informatics

BACKGROUND: The "applied" nature distinguishes applied sciences from theoretical sciences. To emphasize this distinction, we begin with a general, meta-level overview of the scientific endeavor. We introduce the knowledge spectrum and four interconnected modalities of knowledge. In addition to the traditional differentiation between implicit and explicit knowledge we outline the concepts of general and individual knowledge. We connect general knowledge with the "frame problem," a fundamental issue of artificial intelligence, and individual knowledge with another important paradigm of artificial intelligence, case-based reasoning, a method of individual knowledge processing that aims at solving new problems based on the solutions to similar past problems. We outline the fundamental differences between Medical Informatics and theoretical sciences and propose that Medical Informatics research should advance individual knowledge processing (case-based reasoning) and that natural language processing research is an important step towards this goal that may have ethical implications for patient-centered health medicine. DISCUSSION: We focus on fundamental aspects of decision-making, which connect human expertise with individual knowledge processing. We continue with a knowledge spectrum perspective on biomedical knowledge and conclude that case-based reasoning is the paradigm that can advance towards personalized healthcare and that can enable the education of patients and providers. We center the discussion on formal methods of knowledge representation around the frame problem. We propose a context-dependent view on the notion of "meaning" and advocate the need for case-based reasoning research and natural language processing. In the context of memory based knowledge processing, pattern recognition, comparison and analogy-making, we conclude that while humans seem to naturally support the case-based reasoning paradigm (memory of past experiences of problem-solving and powerful case matching mechanisms), technical solutions are challenging. Finally, we discuss the major challenges for a technical solution: case record comprehensiveness, organization of information on similarity principles, development of pattern recognition and solving ethical issues. SUMMARY: Medical Informatics is an applied science that should be committed to advancing patient-centered medicine through individual knowledge processing. Case-based reasoning is the technical solution that enables a continuous individual knowledge processing and could be applied providing that challenges and ethical issues arising are addressed appropriately

Un modelo de Razonamiento Basado en Casos para la Captación de Requisitos en el desarrollo de proyectos de software

Los estudios muestran que una de las causas de los atrasos de los proyectos de software se encuentra en la Captación de Requisitos. Se sabe que el costo para reparar un error en los requisitos es 5 a 10 veces menos que en la codificación y 200 veces menos que en el mantenimiento. Existen muchos esfuerzos para mitigar este problema, como por ejemplo: herramientas y técnicas que facilitan el trabajo en la administración y gestión de requisitos, tales como REM, Entrevistas, Encuestas, Casos de Usos, Win Win, etc. Sin embargo, según los últimos reportes aún se mantienen los problemas. Es por ello que en el presente trabajo proponemos Un Modelo de Razonamiento Basado en Casos para la Captación de Requisitos en el desarrollo de proyectos de software. Con esta técnica de inteligencia artificial se aprovecha los requisitos funcionales de proyectos de software desarrollados anteriormente, para luego resolver o identificar requisitos funcionales de un nuevo proyecto. Se realizan dos casos de estudio donde se compara el Modelo CBR propuesto con otras dos técnicas: Casos de Usos y Win Win, se aplica métricas de calidad a los requisitos obtenidos con el modelo propuesto, Casos de Uso y Win Win, finalmente, se ve el éxito de los resultados. Palabras clave: Razonamiento basado en casos, Requisito, Captación de Requisitos.--- Studies show that one of the causes of delay in software projects is in the Collection Requirements. It is also known that the cost to repair an error in the requirements is 5 to 10 times less than in the encoding and 200 times less than in maintenance. Thus there are many efforts to mitigate this problem. Such as: tools and techniques that make working in the administration and management requirements, such as REM, Interviews, Surveys, Case Uses, Win Win, etc. However according to the latest reports are the problems. That is why in this paper we propose a model of Case-Based Reasoning for Collecting Requirements in the software development project. With this artificial intelligence technique, is used the functional requirements of software projects previously developed to solve or identify functional requirements of a new project. There are two case studies, which compares the proposed CBR model with two other techniques: Use Cases and Win Win. Quality Metrics Applicable requirements obtained with the proposed model, Use Cases and Win Win, finally see the successful outcome. Keywords: Case-based reasoning, Requirement, Requirements Elicitation.Tesi

Sistema de razonamiento basado en casos, para la mejora de atención de salud en un centro rural

Diseña un modelo de sistema utilizando el razonamiento basado en casos (RBC) como apoyo al médico, para diagnosticar enfermedades más comunes en pobladores de un centro rural, con la finalidad de paliar en parte las necesidades básicas de salud en aquellos lugares.Tesi

Case-based learning of plans and goal states in medical diagnosis

We introduce a case-based system, BOLERO, that learns both plans and goal states. The major aim is that of improving the performance of a rule-based diagnosis system by adapting its behavior using the most recent information available about a patient. On the one hand BOLERO gets knowledge from cases in the form of diagnostic plans that are represented as sequences of decision steps. The advantages of this representation include: (1) retrieval and adaptation of parts of plans (steps) appropriate to the current problem state; (2) generation of new plans not previously available in memory; and (3) learning from experience, both from successful or failed plans. On the other hand, since goal states are sets of final diagnosis likelihoods they are not known beforehand, i.e. goal states are not defined and the system has to learn to recognize them. For this reason BOLERO has a case-based method that uses solutions of past cases to recognize a diagnostic state as a goal state of a new planning problem. BOLERO and a rule-based system are integrated into a meta-level architecture in which we emphasize the collaboration of both systems in solving problems. The rule-based system executes the plans generated by BOLERO. As a consequence of the execution of plans, the rule-based system furnishes BOLERO with new information with which BOLERO can generate a new plan to adapt the reasoning process of the rule-based system into correspondence with the recent available data. All the methods have been designed to be useful for medical diagnosis and have been tested in the domain of diagnosing pneumonia. | BOLERO is a case-based system that learns both plans and goal states in order to improve the performance of a rule-based diagnosis system by adapting its behavior using the most recent information available about a patient. BOLERO gets knowledge from cases in the form of diagnostic plans that are represented as sequences of decision steps. The advantages of this representation include: (1) retrieval and adaptation of parts of plans (steps) appropriate to the current problem state; (2) generation of new plans not previously available in memory; and (3) learning from experience, both from successful or failed plans. All the methods have been designed to be useful for medical diagnosis and have been tested in the domain of diagnosing pneumonia.Peer Reviewe