Decision support methods in diabetic patient management by insulin administration neural network vs. induction methods for knowledge classification

Diabetes mellitus is now recognised as a major worldwide public health problem. At present, about 100 million people are registered as diabetic patients. Many clinical, social and economic problems occur as a consequence of insulin-dependent diabetes. Treatment attempts to prevent or delay complications by applying ‘optimal’ glycaemic control. Therefore, there is a continuous need for effective monitoring of the patient. Given the popularity of decision tree learning algorithms as well as neural networks for knowledge classification which is further used for decision support, this paper examines their relative merits by applying one algorithm from each family on a medical problem; that of recommending a particular diabetes regime. For the purposes of this study, OC1 a descendant of Quinlan’s ID3 algorithm was chosen as decision tree learning algorithm and a generating shrinking algorithm for learning arbitrary classifications as a neural network algorithm. These systems were trained on 646 cases derived from two countries in Europe and were tested on 100 cases which were different from the original 646 cases

Agents in Medical Informatics

Edited by Colin Fyfe (Ed), Paisley, UK, ICSC</p

Patient Care Management Using A Multi-Agent Approach

ISBN: 0-7803-6584-4, Pages 564 - 567,</p

A Multimedia Interactive Education System for Prostate Cancer Patients: Development and Preliminary Evaluation

BACKGROUND: A cancer diagnosis is highly distressing. Yet, to make informed treatment choices patients have to learn complicated disease and treatment information that is often fraught with medical and statistical terminology. Thus, patients need accurate and easy-to-understand information. OBJECTIVE: To introduce the development and preliminary evaluation through focus groups of a novel highly-interactive multimedia-education software program for patients diagnosed with localized prostate cancer. METHODS: The prostate interactive education system uses the metaphor of rooms in a virtual health center (ie, reception area, a library, physician offices, group meeting room) to organize information. Text information contained in the library is tailored to a person's information-seeking preference (ie, high versus low information seeker). We conducted a preliminary evaluation through 5 separate focus groups with prostate cancer survivors (N = 18) and their spouses (N = 15). RESULTS: Focus group results point to the timeliness and high acceptability of the software among the target audience. Results also underscore the importance of a guide or tutor who assists in navigating the program and who responds to queries to facilitate information retrieval. CONCLUSIONS: Focus groups have established the validity of our approach and point to new directions to further enhance the user interface