OTE-TS--a new value-added telematics service for telemedicine applications.

A new telemedicine service denoted as the Hellenic Telecommunication Organization Telemedicine Service (OTE-TS), which addresses an open multidisciplinary group of medical care providers (users), is presented in this paper. OTE-TS is supported by the Hellenic Telecommunication Organization (OTE) and is the result of the close collaboration of engineers, medical doctors, and market analysts. Its architecture provides the user with an integrated electronic working domain handling diverse types of conversational audiovisual information and medical records. It includes a stack of functions, protocols, and interfaces for data acquisition, processing, and display, and for the composition of dial-up multiparty cooperation schemes (synchronous or asynchronous), suitable for the coordination and management of high-level consult, report, and review activities. The service provider (i.e., the OTE) performs the central administration and maintenance of the service, as well as the management of medical cases and reports that are exchanged among the users. The service has been extensively tested in real-world conditions.

A new concept toward computer-aided medical diagnosis--a prototype implementation addressing pulmonary diseases.

The original concept that led to the structuring of a computer-based medical decision support system (DSS) that is able to support a physician's diagnosis is introduced in this paper. The concept's implementation modeled a generic DSS, the core of which are an integrated knowledge/information base (KIB) along with the inference properties of a data evaluator. The KIB encapsulates the necessary medical knowledge and experience in the form of rules and constraints, preemptive tasks, and actual patients' clinical data. The data evaluator handles approved medical subjective and objective criteria for assessing the KIB's data. The data evaluator incorporates a medical standard data gathering and decision process, structured upon the principles of the clinical differential diagnosis methodology and has been integrated in the system by means of both algorithmic and artificial intelligence techniques. The novel model and the resulted computer-based package have been extensively tested under the Pulmonary Department, University Regional Hospital Patras, Patras, Greece.

A new perspective in learning pattern generation for teaching neural networks.

This article deals with the Learning Patterns (LPs)' generation, a major aspect of Feed-Forward Artificial Neural Networks (FANNs)' learning process. Currently, more work is done to understand the mechanisms and improve the speed, learning accuracy, and implementation features of FANNs' teaching algorithms, though little is done towards the development of enhanced techniques that would extract experts' knowledge (from examples, rules, etc.) and obtain standardised LPs that would improve this learning process. A new approach in generating LPs is thereby introduced, that is used to train a new Medical Decision Support System (MDSS) based on FANNs, and its performance is analysed and compared with previous methods. It can handle incomplete data archives, individually boost any particular dataum special characteristics, and its application induces the FANNs to show better convergent facets. The efficiency of the resulting MDSS was thoroughly tested by pulmonologists and haematologists using medical data archives of a regional hospital.