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Biomedical subjects

F Azuaje

Publications and source records attributed to F Azuaje.

6 recordsLinked to original sources
PubMed2001

A computational neural approach to support the discovery of gene function and classes of cancer.

Advances in molecular classification of tumours may play a central role in cancer treatment. Here, a novel approach to genome expression pattern interpretation is described and applied to the recognition of B-cell malignancies as a test set. Using cDNA microarrays data generated by a previous study, a neural network model known as simplified fuzzy ARTMAP is able to identify normal and diffuse large B-cell lymphoma (DLBCL) patients. Furthermore, it discovers the distinction between patients with molecularly distinct forms of DLBCL without previous knowledge of those subtypes.

Algorithms↗
PubMed1999

Predicting coronary disease risk based on short-term RR interval measurements: a neural network approach.

Coronary heart disease is a multifactorial disease and it remains the most common cause of death in many countries. Heart rate variability has been used for non-invasive measurement of parasympathetic activity and prediction of cardiac death. Patterns of heart rate variability associated with respiratory sinus arrhythmia have recently been considered as possible indicators of coronary heart disease risk in asymptomatic subjects. The aim of this work is to detect individuals at varying risk of coronary heart disease based on short-term heart rate variability measurements under controlled respiration. Artificial neural networks are used to recognise Poincaré-plot-encoded heart rate variability patterns related to coronary heart disease risk. The results indicate a relatively coarse binary representation of Poincaré plots could be superior to an analogue encoding which, in principle, carries more information.

Coronary Disease↗
PubMed1999

Improving clinical decision support through case-based data fusion.

This paper presents an information fusion technique based on a knowledge discovery model, and the case-based reasoning decision framework. Using signal data and database records from the heart disease risk estimation domain, three data fusion methods are discussed. Two of these methods combine information at the retrieval-outcome level, and one method merges data at the discovery-input level. The result of these three models are compared and evaluated against the performance of single-source models. It is shown that the methods that fuse information at the retrieval-outcome level are significantly superior.

Adult↗