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W Dubitzky

Publications and source records attributed to W Dubitzky.

4 recordsLinked to original sources
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↗
PubMedDate not supplied

Knowledge discovery in biology and biotechnology texts: a review of techniques, evaluation strategies, and applications.

Arguably, the richest source of knowledge (as opposed to fact and data collections) about biology and biotechnology is captured in natural-language documents such as technical reports, conference proceedings and research articles. The automatic exploitation of this rich knowledge base for decision making, hypothesis management (generation and testing) and knowledge discovery constitutes a formidable challenge. Recently, a set of technologies collectively referred to as knowledge discovery in text (KDT) has been advocated as a promising approach to tackle this challenge. KDT comprises three main tasks: information retrieval, information extraction and text mining. These tasks are the focus of much recent scientific research and many algorithms have been developed and applied to documents and text in biology and biotechnology. This article introduces the basic concepts of KDT, provides an overview of some of these efforts in the field of bioscience and biotechnology, and presents a framework of commonly used techniques for evaluating KDT methods, tools and systems.

Algorithms↗