Managing urinary incontinence through hand-held real-time decision support aid

Authors:
Constantinos Koutsojannis;Chrysa Lithari;Ioannis Hatzilygeroudis
Affiliations:
Technological Educational Institute of Patras, Physiotherapy Department, Psaron 6, 25100 Aigion, Greece and University of Patras, Computer Engineering & Informatics Department, 26500 Rion, Patras, ...;University of Patras, Department of Medicine, 26500 Rion, Patras, Greece;University of Patras, Computer Engineering & Informatics Department, 26500 Rion, Patras, Greece
Venue:
Computer Methods and Programs in Biomedicine
Year:
2012

Citing 3
Cited 0

HIROFILOS: a medical expert system for prostate diseases

CIMMACS'08 Proceedings of the 7th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
A GA driven intelligent system for medical diagnosis

KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Fuzzy-evolutionary synergism in an intelligent medical diagnosis system

KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present an intelligent system for the diagnosis and treatment of urinary incontinence (UI) for males as well as females, called e-URIN. e-URIN is an intelligent system for diagnosis and treatment of urinary incontinence according to symptoms that are realized in one patient and usually recorded through his clinical examination as well as specific test results. The user-friendly proposed intelligent system is accommodated on a hospital server supporting e-health tools, for use through pocket PCs under wireless connection as a decision support system for resident doctors, as well as an educational tool for medical students. It is based on expert system knowledge representation provided from urology experts in combination with rich bibliographic search and study ratified with statistical results from clinical practice. Preliminary experimental results on a real patient hospital database provide acceptable performance that can be improved using more than one computational intelligence approaches in the future.