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A position paper on predicting the onset of nocturnal enuresis using advanced machine learning

Fergus, P and Hussain, A and Al-Jumeily, D and Radi, N (2015) A position paper on predicting the onset of nocturnal enuresis using advanced machine learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9226. pp. 689-700. ISSN 0302-9743

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Abstract

Bed-wetting during normal sleep in children and young people has a significant impact on the child and their parents. The condition is known as nocturnal enuresis and its underlying cause has been subject to different explanatory factors that include, neurological, urological, sleep, genetic and psychosocial influences. Several clinical and technological interventions for managing nocturnal enuresis exist that include the clinician’s opinions, pharmacology interventions, and alarm systems. However, most have failed to produce any convincing results. Clinical information is often subjective and often inaccurate, the use of desmopressin and tricyclic antidepressants only report between 20 % and 40 % success, and alarms only a 50 % success fate. This position paper posits an alternative research idea concerned with the early detection of impending involuntary bladder release. The proposed framework is a measurement and prediction system that processes moisture and bladder volume data from sensors fitted into undergarments that are used by patients suffering with nocturnal enuresis. The proposed framework represents a level of sophistication in nocturnal enuresis treatment not previously considered.

Item Type: Article
Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-22186-1_68
Uncontrolled Keywords: 08 Information And Computing Sciences
Subjects: R Medicine > RJ Pediatrics
Divisions: Computer Science
Publisher: Springer Verlag
Date Deposited: 23 Nov 2015 13:35
Last Modified: 11 Aug 2016 23:50
DOI or Identification number: 10.1007/978-3-319-22186-1_68
URI: http://researchonline.ljmu.ac.uk/id/eprint/2374

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