Carles Pous

eXiT*CBR: A framework for case-based medical diagnosis development and experimentation

OBJECTIVE Medical applications have special features (interpretation of results in medical metrics, experiment reproducibility and dealing with complex data) that require the development of particular tools. The eXiT*CBR framework is proposed to support the development of and experimentation with new case-based reasoning (CBR) systems for medical diagnosis. METHOD Our framework offers a modular, heterogeneous environment that combines different CBR techniques for different application requirements. The graphical user interface allows easy navigation through a set of experiments that are pre-visualized as plots (receiver operator characteristics (ROC) and accuracy curves). This user-friendly navigation allows easy analysis and replication of experiments. Used as a plug-in on the same interface, eXiT*CBR can work with any data mining technique such as determining feature relevance. RESULTS The results show that eXiT*CBR is a user-friendly tool that facilitates medical users to utilize CBR methods to determine diagnoses in the field of breast cancer, dealing with different patterns implicit in the data. CONCLUSIONS Although several tools have been developed to facilitate the rapid construction of prototypes, none of them has taken into account the particularities of medical applications as an appropriate interface to medical users. eXiT*CBR aims to fill this gap. It uses CBR methods and common medical visualization tools, such as ROC plots, that facilitate the interpretation of the results. The navigation capabilities of this tool allow the tuning of different CBR parameters using experimental results. In addition, the tool allows experiment reproducibility.

eXiT*CBR.v2: Distributed case-based reasoning tool for medical prognosis

In this work we propose a user-friendly medically oriented tool for prognosis development systems and experimentation under a case-based reasoning methodology. The tool enables health care collaboration practice to be mapped in cases where different doctors share their expertise, for example, or where medical committee composed of specialists from different fields work together to achieve a final prognosis. Each agent with a different piece of knowledge classifies the given cases through metrics designed for this purpose. Since multiple solutions for the same case are useless, agents collaborate among themselves in order to achieve a final decision through a coordinated schema. For this purpose, the tool provides a weighted voting schema and an evolutionary algorithm (genetic algorithm) to learn robust weights. Moreover, to test the experiments, the tool includes stratified cross-validation methods which take the collaborative environment into account. In this paper the different collaborative facilities offered by the tool are described. A sample usage of the tool is also provided.

Modeling Reuse on Case-Based Reasoning with Application to Breast Cancer Diagnosis

In the recent years, there has been an increasing interest on the use of case-based reasoning (CBR) in Medicine. CBR is characterized by four phases: retrieve, reuse, revise and retain. The first and last phases have received a lot of attention by the researchers, while the reuse phase is still in its infancy. The reuse phase involves a multi-facet problem which includes dealing with the closeness to the decision threshold used to determine similar cases, among other issues. In this paper, we propose a new reuse method whose decision variable is based on the similarity ratio. We have applied the method and tested in a breast cancer diagnosis database.

Extending a Fault Dictionary Towards a Case Based Reasoning System for Linear Electronic Analog Circuits Diagnosis

There are plenty of methods proposed for analog electronic circuit diagnosis, but the most popular ones are the fault dictionary techniques. Admitting more cases in a fault dictionary can be seen as a natural development towards a CBR system. The proposal of this paper is to extend the fault dictionary towards a Case Based Reasoning system. The case base memory, retrieval, reuse, revise and retain tasks are described. Special attention to the learning process is taken. An application example on a biquadratic filter is shown. The faults considered are parametric, permanent, independent and simple, although the methodology could be extrapolated for catastrophic and multiple fault diagnosis. Also, the method is focused and tested only on passive faulty components. Nevertheless, it can be extended to cover active devices as well.

Diagnosing patients with a combination of principal component analysis and case based reasoning

This paper addresses the application of a principal component analysis (PCA) of categorical data prior to diagnosing a patients dataset using a case-based reasoning (CBR) system. The particularity is that the standard PCA techniques are designed to deal with numerical attributes, but our medical dataset contains many categorical data and alternative methods such as RS-PCA are required. Thus, we propose to hybridize RS-PCA (regular simplex PCA) and a simple CBR system. Results show how the hybrid system, when diagnosing a medical dataset, produces results similar to the ones obtained when using the original attributes. These results are quite promising since they allow diagnosis with less computation effort and memory storage.

A novel communication system approach for a Smart City based on the human nervous system

Abstract The massive introduction of internet, architect of the Internet of Things paradigm, was expected to facilitate the spread of people out of the urban spaces. However, the reality is that increasingly people are moving to cities, which generates many challenges for the cities of tomorrow. In this context, the Smart City concept emerges, where Information and Communication Technologies have a key role. This situation has revealed an important issue that needs to be addressed: the lack of an ubiquitous communication architecture able to deal with the expected requirements of a SC . To this end, a wide review of the existing communication frameworks and city services have been performed, which showed that existing proposals are classical fixed ad-hoc solutions for very specific problems with no resilience and common thread. By rethinking the nature of cities and considering them as living organisms, it is possible to relate the nervous systems with the communication architecture of a city. Therefore, this work proposes a novel communication architecture, ubiquitous and resilient, inspired in the human nervous system by the definition of Smart Gateways , able to satisfy the needs of a real SC and adaptable to the growing and specific requirements of every single city. Additionally, the research also includes the simulation of the communication channel with different technologies.

Case base management for analog circuits diagnosis improvement

There have been some Artificial Intelligence applications developed for electronic circuits diagnosis, but much remains to be done in this field, above all in the analog domain. The purpose of this paper is not to give a general solution, but to contribute with a new methodology. Our aim is to develop a methodology for analog circuit diagnosis based on improving the well-known fault dictionary techniques by means of new cases addition or adaptation towards a Case Based Reasoning system. As an example, a fault dictionary method have been studied in detail. It has been used as starting point for case base construction to be applied to a real electronic circuit. The faults considered are parametric, permanent, independent and simple.

Boosting CBR Agents with Genetic Algorithms

In this paper we present a distributed system in which several case-based reasoning (CBR) agents cooperate under a boosting schema. Each CBR agent knows part of the cases (a subset of the available attributes) and is trained with a subset of the available cases (so not all the agents know the same cases). The solution of the system is then computed by means of a weighted average of the solutions provided by the CBR agents. Weights are actively learnt by a genetic algorithm. The system has been applied to a breast cancer application domain. The results show that with our methodology we can improve the results obtained with a case base in which attributes have been manually selected by physicians, saving physicians work in future.

Diagnosing Patients Combining Principal Components Analysis and Case Based Reasoning

This paper addresses the application of a PCA analysis on categorical data prior to diagnose a patients data set using a Case-Based Reasoning (CBR) system. The particularity is that the standard PCA techniques are designed to deal with numerical attributes, but our medical data set contains many categorical data and alternative methods as RS-PCA are required. Thus, we propose to hybridize RS-PCA (Regular Simplex PCA) and a simple CBR. Results show how the hybrid system produces similar results when diagnosing a medical data set, that the ones obtained when using the original attributes. These results are quite promising since they allow to diagnose with less computation effort and memory storage.

Multi criteria decision methods for coordinating case-based agents

There is an increasing interest on ensemble learning since it reduces the bias-variance problem of several classifiers. In this paper we approach an ensemble learning method in a multi-agent environment. Particularly, we use genetic algorithms to learnt weights in a boosting scenario where several case-based reasoning agents cooperate. In order to deal with the genetic algorithm results, we propose several multicriteria decision making methods. We experimentally test the methods proposed in a breast cancer diagnosis database.