A case-based reasoning framework for prediction of stroke

© Springer Nature Singapore Pte Ltd. 2018. Case-based reasoning (CBR) has been a popular method in health care sector from the last two decades. It is used for analysis, prediction, diagnosis and recommending treatment for patients. This research purposes a conceptual CBR framework for stroke disease prediction that uses previous case-based knowledge. The outcomes of this approach not only assist in stroke disease decision-making, but also will be very useful for prevention and early treatment of patients

Prioritizing Offshore Vendor Selection Criteria for the North American Geospatial Industry

The U.S. market for geospatial services totaled US $2.2 billion in 2010, representing 50% of the global market. Data-processing firms subcontract labor-intensive portions of data services to offshore providers in South and East Asia and Eastern Europe. In general, half of all offshore contracts fail within the first 5 years because one or more parties consider the relationship unsuccessful. Despite the high failure rates, no study has examined the offshore vendor selection process in the geospatial industry. The purpose of this study was to determine the list of key offshore vendor selection criteria and the efficacy of the analytic hierarchy process (AHP) for ranking the criteria that North American geospatial companies consider in the offshore vendor selection process. After the selection of the initial list of factors from the literature and their validation in a pilot study, a final survey instrument was developed and administered to 15 subject matter experts (SMEs) in North America. The SMEs expressed their preferences for one criterion over another by pairwise comparisons, which served as input to the AHP procedure. The results showed that the quality of deliverables was the top ranked (out of 26) factors, instead of the price, which ranked third. Similarly, SMEs considered social and environmental consciousness on the vendor side as irrelevant. More importantly, the findings indicated that the structured AHP process provides a useful and effective methodology whose application may considerably improve the quality of the overall vendor selection process. Last, improved and stabilized business relationships leading to predictable budgets might catalyze social change, supporting stable employment. Consumers could benefit from derivative improvements in product quality and pricing

Sistema genérico de razonamiento basado en casos (CBR) multi-clase como soporte al diagnóstico médico mediante técnicas de reconocimiento de patrones

[EN]Learning from experience is a process that occurs naturally in humans, and the knowledge generated by this process becomes the basis for solutions to everyday problems. In the field of artificial intelligence, specifically in the area of machine learning, aimed at emulating such ability, the methodology called case-based reasoning (CBR) has arisen. The core of a CBR system is the case, usually denoting a previous problem or experience, which has been captured and learned, and can be then reused to solve future problems. The life cycle of a CBR-based system consists of four main stages: Recovery, wherein the problem is identified and past cases similar to the new case are found; Adaptation, wherein a solution is suggested from the recovered cases; Revision, in which the proposed solution is evaluated; And finally learning, wherein the system is updated to learn from experience. The CBR systems have demonstrated their high applicability in the field of health, specifically in medical diagnosis so that the symptoms represent the problem (new case) and, therefore, the solution obtained is to be the recommended diagnosis. In the state of the art of CBR applied to medical diagnosis, there have been developed some studies mainly focusing on improvements of the recovery stage. Nonetheless, there are still some open issues related to case representation and multiclass problem solving. In fact, if the representation of the cases is not adequate, the results of the recovery stage are not expected to be optimal. In addition, most CBR systems have been designed to solve biclass problems, thereby limiting the automatic adaptation stage to two possible solutions (typically, normal or pathological). Then such systems are not able to categorize the condition of a pathology nor to identify differential diagnoses. In this thesis, a proposal of a generic CBR system for the identification of multiple diagnostic cases using improved recovery and adaptation stages is presented. For this purpose, SAM (Improved Adaptation System) is proposed, which consists of a system that uses two cascade classifiers that improves the classification performance of ill patients. This proposal arises as a result of a comparative study of data representation techniques to obtain the case vector and different multiclass classifiers for the adaptation stage. In addition, as a significant contribution of this work, an interface is developed that communicates to the specialist the belonging probabilities of the new case to each of the possible diagnoses. Experimentally, it is verified that SAM -using two classifiers in cascade based on K-NN along with an appropriate selection of characteristics in the pre-process- generates satisfactory results in terms of classification measures while providing the specialist with intelligible results of the case recovery.[ES]El aprendizaje a partir de la experiencia es un proceso que se da de forma natural en los seres humanos, y el conocimiento generado con dicho proceso se convierte en la base para establecer soluciones a problemas cotidianos. En el campo de la inteligencia artificial, específicamente en el área del aprendizaje de máquina, pretendiendo emular esta habilidad del ser humano, ha surgido la metodología denominada razonamiento basado en casos (CBR).El núcleo de un sistema de CBR es el caso, que denota usualmente una situación problema o experiencia previa, la cual ha sido capturada y aprendida, y puede ser reutilizada para resolver problemas futuros. El ciclo de vida de un sistema basado en CBR consiste en cuatro etapas principales: Recuperación, donde se identifica el problema y se encuentran casos pasados similares al nuevo caso; adaptación, donde se sugiere una solución a partir de los casos recuperados; revisión, en la cual se evalúa la solución propuesta; y, finalmente, aprendizaje, donde se actualiza el sistema para aprender de la experiencia. Los sistemas de CBR han demostrado su alta aplicabilidad en el campo de la salud, específicamente en diagnóstico médico de forma que los síntomas representan el problema (nuevo caso) y, por tanto, la solución obtenida será el diagnóstico recomendado. En el estado del arte de CBR aplicado a diagnóstico médico, se encuentran algunos estudios que principalmente se enfocan en mejoras de la etapa de recuperación. No obstante, aún existen problemas abiertos relacionados con la representación de los casos y la solución de problemas multiclase. En efecto, si la representación de los casos no es adecuada, los resultados de la recuperación no serán óptimos. Además, la mayoría de los sistemas de CBR han sido diseñados para resolver problemas biclase, limitando entonces la etapa de adaptación automática a dos únicas posibles soluciones (típicamente, normal o patológico), con lo cual dichos sistemas pierden la capacidad de categorizar el estado de una patología o de identificar diagnósticos diferenciales. En este trabajo de tesis, se presenta una propuesta de sistema genérico de CBR para la identificación de múltiples casos diagnósticos usando etapas de recuperación y adaptación mejoradas. Para este propósito, se plantea SAM (Sistema de Adaptación Mejorada) que consiste en un sistema que utiliza dos clasificadores en cascada que mejora el desempeño de la clasificación de los pacientes enfermos. Dicha propuesta surge como resultado de un estudio comparativo de técnicas de representación de datos para obtener el vector de casos y de diferentes clasificadores multiclase en la etapa de adaptación. Además, como aporte significativo de este trabajo, se desarrolla una interfaz que comunica al especialista las probabilidades de pertenencia del nuevo caso a cada uno de los posibles diagnósticos. Experimentalmente, se comprueba que SAM, usando dos clasificadores en cascada basados en K-NN y con una apropiada selección de características en el pre-proceso, genera resultados satisfactorios en términos de medidas de clasificación mientras provee al especialista de forma inteligible los resultados de la recuperación de casos