A Model for Predicting the Amount of Urine in the Bladder Based on App-generated Tracking Data

Title data

Fechner, Pascal ; Lockl, Jannik ; Ruhland, Nicolas ; Zürl, Tristan ; Zwede, Till:
A Model for Predicting the Amount of Urine in the Bladder Based on App-generated Tracking Data.
In: Park, Taesung (ed.): 2020 IEEE International Conference on Bioinformatics and Biomedicine : Proceedings. - Piscataway, NJ : IEEE , 2020 . - pp. 2952-2954
ISBN 978-1-7281-6215-7
DOI: https://doi.org/10.1109/BIBM49941.2020.9313381

Official URL:

Abstract in another language

Incontinence patients suffering from neurogenic bladder dysfunction lack information about the filling level of their urinary bladder. A real-time prediction of the filling level of their bladder could support them managing their daily routines. In this study, we developed a system that predicts the bladder filling level based on user-tracked fluid intake. The system collects and analyzes the data to predict the current filling level of the bladder. Displayed in an app, users can optimize their micturition frequency receiving an alert when a critical level is reached. In the same way users can compare the predicted and their target filling level.

Further data

Item Type:	Article in a book
Refereed:	Yes
Keywords:	app; chronic disease management; data analytics; eHealth; inContAlert; incontinence; prediction
Institutions of the University:	Faculties > Faculty of Law, Business and Economics > Department of Business Administration Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XVII - Information Systems and Value-Based Business Process Management Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XVII - Information Systems and Value-Based Business Process Management > Chair Information Systems and Value-Based Business Process Management - Univ.-Prof. Dr. Maximilian Röglinger Research Institutions Research Institutions > Affiliated Institutes Research Institutions > Affiliated Institutes > Fraunhofer Project Group Business and Information Systems Engineering Research Institutions > Affiliated Institutes > FIM Research Center Finance & Information Management Faculties Faculties > Faculty of Law, Business and Economics
Result of work at the UBT:	Yes
DDC Subjects:	000 Computer Science, information, general works > 004 Computer science 300 Social sciences > 330 Economics
Date Deposited:	15 Dec 2020 09:15
Last Modified:	21 May 2021 06:34
URI:	https://eref.uni-bayreuth.de/id/eprint/61128