Background: The performance of separate Intensive Care Unit (ICU) status scoring systems vis-à-vis prediction of outcome is not satisfactory. Computer-based predictive modeling techniques may yield good results but their performance has seldom been extensively compared to that of other mature or emerging predictive models. The objective of the present study was twofold: to propose a prototype meta-level predicting approach concerning Intensive Care Unit (ICU) survival and to evaluate the effectiveness of typical mining models in this context.

This work considering the telecare environment as a multi-dimensional, operational organization has put the focus on accurate telecare services' design and redesign. The parameters are not limited, by any means, and are drawn from experience of designing services in a variety of telecare domains. The optimal parameter combination must be chosen according to the aim of each telecare procedure. Further research is needed to determine the minimum parameters to support telecare service design.

In an e-health cardiology environment, the current knowledge engineering systems can support two knowledge processes; the knowledge tracing, and the knowledge cataloguing. We have developed an n-tier system capable of supporting these processes by enabling human collaboration in each phase along with, a prototype scalable knowledge engineering tactic. A knowledge graph is used as a dynamic information structure. Biosignal data (values of HR, QRS, and ST variables) from 86 patients were used; two general practitioners defined and updated the patients' clinical management protocols; and feedback was inserted retrospectively. Several calibration tests were also performed. The system succeeded in formulating three knowledge catalogues per patient, namely, the "patient in life", the "patient in time", and the "patient in action". For each patient the clinically accepted normal limits of each variable were predicted with an accuracy of approximately 95%. The patients' risk-levels were identified accurately, and in turn, the errors were reduced. The data and the expert-oriented feedback were also time-stamped correctly and synchronized under a common time-framework. Knowledge processes optimization necessitates human collaboration and scalable knowledge engineering tactics. Experts should be responsible for resenting or rejecting a process if it downgrades the provided healthcare quality.

Telehealth is the exchange of health information and the provision of health care services through electronic information and communications technology, where participants are separated by geographic, time, social and cultural barriers. The shift of telemedicine from desktop platforms to wireless and mobile technologies is likely to have a significant impact on healthcare in the future. It is therefore crucial to develop a general information exchange e-medical system to enables its users to perform online and offline medical consultations through diagnosis. During the medical diagnosis, image analysis techniques combined with doctor's opinions could be useful for final medical decisions. Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. In medical images, segmentation has traditionally been done by human experts. Even with the aid of image processing software (computer-assisted segmentation tools), manual segmentation of 2D and 3D CT images is tedious, time-consuming, and thus impractical, especially in cases where a large number of objects must be specified. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. The main purpose of this work is to analyze segmentation techniques for the definition of anatomical structures under telemedical systems.

Multidisciplinary collaboration is a key requirement in several contemporary interventional radiology procedures (IRPs). We proposed a hybrid system (NetAngio) to enable "on the fly" heterogeneous collaboration to support IRP providing intraoperating essential services, and investigate its feasibility and effectiveness in a referral medical center. We have developed a Web-based, cost-effective structure, able to support real-time mentoring, image manipulation, and education services beyond the boundaries of the single institution and potentially allow sub specialists to participate in opinion and decision making in more complex cases. Supported services based on a Motion Joint Photographic Experts Group (MJPEG) coder/decoder (CODEC) can be easily accessible by authorized collaborators, within a user-friendly interface by using a typical Web-browser. Ten interventional radiologists, two vascular surgeons and two medical physicists participated in 33 "fully collaborative" cases during a 13-month period from January 2004 to February 2005. In addition, fifteen 90-minute open seminars and finally, 75 expert's module activations, and 255 learner's module activations were performed during the evaluation period. Collaborative procedures are able to enhance outcomes performance especially in more complex cases where the simultaneous presence of a remote expert interventionist and a medical physicist or a surgeon is required. Further research is needed to promote integration of additional data sources and services.