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Improving the Efficacy of Enuresis Treatment through Early Prediction of Treatment Outcome: A Machine Learning Approach

Jönsson, Karl-Axel LU and Andersson, Edvin LU (2023) EEML05 20231
Department of Biomedical Engineering
Abstract
Bedwetting, also known as enuresis, is the second most common
chronic health problem among children and it affects their
everyday life negatively. A treatment option for enuresis is an
alarm treatment that the patient tries for 6-8 weeks before a
decision is made regarding if the treatment should continue. This
treatment has been further developed by the company Pjama AB
that, in addition to the alarm, offers a mobile application where
users fill in data about the patient and information about each
night throughout the treatment. We used the machine learning
model random forest to see if predictions of treatment outcome
could be made in early stages of treatment and shorten the
evaluation time based on data from 611 patients.... (More)
Bedwetting, also known as enuresis, is the second most common
chronic health problem among children and it affects their
everyday life negatively. A treatment option for enuresis is an
alarm treatment that the patient tries for 6-8 weeks before a
decision is made regarding if the treatment should continue. This
treatment has been further developed by the company Pjama AB
that, in addition to the alarm, offers a mobile application where
users fill in data about the patient and information about each
night throughout the treatment. We used the machine learning
model random forest to see if predictions of treatment outcome
could be made in early stages of treatment and shorten the
evaluation time based on data from 611 patients. This was done
by using and analyzing data from patients who had used the
Pjama application. The patients were split into training and
testing groups to evaluate to what extent the algorithm could
make predictions every day about whether a patient’s treatment
would become successful, partially successful or unsuccessful.
The results show that a large number of patient outcomes can
be predicted with great accuracy already in the early stages of
the treatment. This enables the correct measures to be taken
earlier in the treatment, including increasing motivation, adding
complementary treatment or terminating treatment. This has the
potential to shorten the treatment as a whole, and to detect
patients who will not respond to the treatment early on, which
in turn can improve the lives of children suffering from enuresis.
The results show a great potential in making the treatment of
enuresis more efficient. (Less)
Please use this url to cite or link to this publication:
author
Jönsson, Karl-Axel LU and Andersson, Edvin LU
supervisor
organization
course
EEML05 20231
year
type
M2 - Bachelor Degree
subject
language
English
id
9121807
date added to LUP
2023-06-26 11:33:11
date last changed
2023-06-26 14:12:50
@misc{9121807,
  abstract     = {{Bedwetting, also known as enuresis, is the second most common
chronic health problem among children and it affects their
everyday life negatively. A treatment option for enuresis is an
alarm treatment that the patient tries for 6-8 weeks before a
decision is made regarding if the treatment should continue. This
treatment has been further developed by the company Pjama AB
that, in addition to the alarm, offers a mobile application where
users fill in data about the patient and information about each
night throughout the treatment. We used the machine learning
model random forest to see if predictions of treatment outcome
could be made in early stages of treatment and shorten the
evaluation time based on data from 611 patients. This was done
by using and analyzing data from patients who had used the
Pjama application. The patients were split into training and
testing groups to evaluate to what extent the algorithm could
make predictions every day about whether a patient’s treatment
would become successful, partially successful or unsuccessful.
The results show that a large number of patient outcomes can
be predicted with great accuracy already in the early stages of
the treatment. This enables the correct measures to be taken
earlier in the treatment, including increasing motivation, adding
complementary treatment or terminating treatment. This has the
potential to shorten the treatment as a whole, and to detect
patients who will not respond to the treatment early on, which
in turn can improve the lives of children suffering from enuresis.
The results show a great potential in making the treatment of
enuresis more efficient.}},
  author       = {{Jönsson, Karl-Axel and Andersson, Edvin}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Improving the Efficacy of Enuresis Treatment through Early Prediction of Treatment Outcome: A Machine Learning Approach}},
  year         = {{2023}},
}