Catálogo de publicaciones - libros

The aim of this paper is to present a new tool of multiple instance learning which is designed using a grammar based genetic programming (GGP) algorithm. We study its application in Web Mining framework to identify web pages interesting for the users. This new tool called GGP-MI algorithm is evaluated and compared with other available algorithms which extend a well-known neighborhood based algorithm (k-nearest neighbour algorithm) to multiple instance learning. Computational experiments show that, the GGP-MI algorithm obtains competitive results, solves problems of other algorithms, such as sparsity and scalability and adds comprehensibility and clarity in the knowledge discovery process.

Assets Prognostics and Health Management (PHM) is a promising application area for Soft Computing (SC). To better understand PHM requirements, we introduce a decision-making framework in which we analyze PHM decisional tasks. This framework is the cross product of the decision’s and the used by SC models. Within such a framework, we analyze the progression from simple to annotated lexicon, morphology, syntax, semantics, and pragmatics. We use this metaphor to monitor the leverage of domain knowledge in SC to perform anomaly detection, anomaly identification, failure mode analysis (diagnostics), estimation of remaining useful life (prognostics), on-board control, and off board logistics actions. We illustrate a case study in anomaly detection, which is solved by the construction and fusion of an ensemble of diverse detectors, each of which is based on different SC technologies.

In this paper we describe how the self-organizing map can be applied to the visualization of the evolution and change of Human Immunodeficiency Virus (HIV) quasiespecies, and how this could be converted into a predictive tool. A SOM is trained with a set of nucleic acid sequences from the HIV-1 virus, and the U-Matrix method is applied to discover what natural groups are formed within it. Results show the validity of the method, and allow to discover two groupings within these sets, and what is the evolutionary path taken in them.

This paper presents a method for estimation of parameters in dynamical systems, applied to a model of the HIV-AIDS epidemics in Cuba. This estimation technique, based upon artificial neural networks, has been successfully applied to robotic systems, whereas the application to epidemiological models is challenged by the possible uncertainty of the model; besides, a state variable exists that is not directly measurable. With regard to the first limitation, a model provided by experts, previously validated by statistical techniques, has been used; with respect to the second drawback, an evaluation of the unknown variable has been carried out from comparisons with other models of the development of the disease. Among the parameters that intervene in the model, three important factors have been considered: the detection rate of the disease, through the contact tracing program; the detection rate through other methods; and the rate of transition to AIDS of previously undetected infected individuals. Results are plausible, according to experts, and they support both the estimation method and the model.

Selective attention to visual-spatial stimuli causes decrements of power in alpha band and increments in beta. For steady-state visual evoked potentials (SSVEP) selective attention affects electroencephalogram (EEG) recordings, modulating the power in the range 8-27 Hz. The same behaviour can be seen for auditory stimuli as well, although for auditory steady-state response (ASSR), it is not fully confirmed yet. The design of selective attention based brain-computer interfaces (BCIs) has two major advantages: First, no much training is needed. Second, if properly designed, a steady-state response corresponding to spectral peaks can be elicited, easy to filter and classify. In this paper we study the behaviour of ANNs as classifiers for a selective attention to auditory stimuli based BCI system.

Evidence-based medicine has grown in stature over the last three decades and is now regarded a key development of modern medicine. The evidence base can be heterogeneous, involving both qualitative knowledge and measured quantitative data. Machine Learning (ML) methods have also begun to establish themselves as an alternative and promising approach to computer-based data analysis in oncology, as this field moves gradually away from being the preserve of traditional statistical analysis. In this paper, we describe the main areas of cancer research in which ML methods are currently being applied, and briefly discuss some of the advantages and disadvantages of their application.

DNA copy number amplifications are hallmarks of many cancers. In this work we analyzed data of genome-wide DNA copy number amplifications collected from more than 4500 neoplasm cases. Based on the 0-1 representation of the data, we trained finite mixtures of multivariate Bernoulli distributions using the EM algorithm to describe the inherent structure in the data. The resulting component distributions of the mixtures of Bernoulli distributions yielded plausible and localized amplification patterns. Individual amplification patterns were tested for their role in cancer groups formed with known risk associations. Our detailed analysis of chromosome 1 showed that asbestos-exposure related and hormonal imbalance-associated cancers were clustered and specific chromosome bands, 1p34 and 1q42, were identified. These sites contain cancer genes, which might explain the condition-specific selection of these loci for amplification.

Approximately 50% of all patients with intraocular melanoma die of metastatic disease, despite successful treatment of the primary tumour. The main factors associated with mortality include: tumour diameter; ciliary body involvement; extraocular tumour spread; epithelioid cell type; high mitotic rate; and chromosome 3 loss. We report the development of a web-based prognostic tool, which integrates all these factors. The cohort comprised 2655 patients (1369 male, 1286 female, mean age: 60.86 years) with histopathological data on 1282 patients and cytogenetic information on 405 patients. There were 871 deaths, 517 of which were from metastatic disease. A Conditional Hazard Estimating Neural Network (CHENN) model has been developed, and used to model the survival probability conditioned on observed clinical data. Such model is trained in the Bayesian framework, which allows model training, model regularization, model comparison and feature selection. The CHENN model is nonlinear, embeds the Cox proportional hazards model, and can correct Cox estimates in the cases with a nonlinear relationship between covariates and survival probabilities, and/or the proportional hazards assumption is not verified. As a result of these studies, we can confidently reassure many patients with intraocular melanoma that their survival probability is not significantly worse than that of the general population. This improves their well-being and avoids unnecessary and expensive screening tests. Conversely, we can reliably identify those with a high risk of metastatic death. These patients are referred to an oncologist for systemic screening and several have undergone partial hepatectomy with significant prolongation of life. Our prognostication enhances the feasibility of future randomized, prospective studies evaluating protocols for systemic screening and adjuvant therapy.

This work evaluates the efficiency of in conjunction with neural network classifiers to detect microcalcification clusters in digitized mammograms, the most important non invasive sign of breast cancer. The widespread was used as the source for digitized mammograms. The results seem to suggest that this technique is suitable to deal with the noisy mammogram environment.

This paper describes an integrated clinical support system combining data entry and access with prognostic modelling, for use by the clinician, complemented by a patient information system tailored to the particular characteristics of the individual patient. The core of the system comprises a modelling methodology based on the PLANN-ARD neural network which combines risk staging with automatic rule generation to derive an explanation facility for the risk group allocation of each patient. The aim of the system is to promote better informed decision making on the part of both the clinician and the patient, exploiting the combined potential of analytical methodologies and the internet.