Biomedical subjects

Norman Black

Publications and source records attributed to Norman Black.

5 recordsLinked to original sources

PubMed2005

Implementing autonomy in a diabetes management system.

We have developed a speech-based telemedicine system which enables patients with hypertension and type 2 diabetes mellitus to send frequent, home-monitored health data via the telephone to the point of care. The decision support module in the system was tested using data from a cohort of 10 patients generated over a two-year period. Results from the tests indicate that the system is effective in providing personalized feedback to the patient and in generating alerts for the clinical user. The work suggests that this method of care delivery is practical, informative, and may improve the efficiency of chronic health-care delivery by reducing costs and improving patient-physician communication between hospital visits.

Chronic Disease↗

PubMed2004

An integrative and interactive framework for improving biomedical pattern discovery and visualization.

Recent progress in medical sciences has led to an explosive growth of data. Due to its inherent complexity and diversity, mining such volumes of data to extract relevant knowledge represents an enormous challenge and opportunity. Interactive pattern discovery and visualization systems for biomedical data mining have received relatively little attention. Emphasis has been traditionally placed on automation and supervised classification problems. Based on self-adaptive neural networks and pattern-validation statistical tools, this paper presents a user-friendly platform to support biomedical pattern discovery and visualization. It has been tested on several types of biomedical data, such as dermatology and cardiology data sets. The results indicate that in comparison to traditional techniques, such as Kohonen Maps, this platform may significantly improve the effectiveness and efficiency of pattern discovery and classification tasks, including problems described by several classes. Furthermore, this study shows how the combination of graphical and statistical tools may make these patterns more meaningful.

Algorithms↗

PubMed2004

Power to the patient, using DI@L-log.

Chronic care patients are demanding to be more actively involved in the care of their condition. Self-monitoring of blood glucose and blood pressure is strongly advocated for people with type 2 diabetes. We are developing a DI@L-log system that replaces the traditional paper logbook used by diabetes patients, enabling them to send their data to the point of care on a weekly basis using spoken dialogue technologies over the telephone. The motivation for our system is to enhance care by providing patients with a voice and therefore empowering individuals to become active self-managers of diabetes.

Blood Glucose Self-Monitoring↗

PubMed2003

A markup language for electrocardiogram data acquisition and analysis (ecgML).

BACKGROUND: The storage and distribution of electrocardiogram data is based on different formats. There is a need to promote the development of standards for their exchange and analysis. Such models should be platform-/ system- and application-independent, flexible and open to every member of the scientific community. METHODS: A minimum set of information for the representation and storage of electrocardiogram signals has been synthesised from existing recommendations. This specification is encoded into an XML-vocabulary. The model may aid in a flexible exchange and analysis of electrocardiogram information. RESULTS: Based on advantages of XML technologies, ecgML has the ability to present a system-, application- and format-independent solution for representation and exchange of electrocardiogram data. The distinction between the proposal developed by the U.S Food and Drug Administration and ecgML model is given. A series of tools, which aim to facilitate ecgML-based applications, are presented. CONCLUSIONS: The models proposed here can facilitate the generation of a data format, which opens ways for better and clearer interpretation by both humans and machines. Its structured and transparent organisation will allow researchers to expand and test its capabilities in different application domains. The specification and programs for this protocol are publicly available.

Decision Support Techniques↗

PubMed2002

Improving biomolecular pattern discovery and visualization with hybrid self-adaptive networks.

There is an increasing need to develop powerful techniques to improve biomedical pattern discovery and visualization. This paper presents an automated approach, based on hybrid self-adaptive neural networks, to pattern identification and visualization for biomolecular data. The methods are tested on two datasets: leukemia expression data and DNA splice-junction sequences. Several supervised and unsupervised models are implemented and compared. A comprehensive evaluation study of some of their intrinsic mechanisms is presented. The results suggest that these tools may be useful to support biological knowledge discovery based on advanced classification and visualization tasks.

Algorithms↗