Authors:
Aleksandar Jeremic
1
;
Dejan Nikolic
2
;
3
;
Milena Santric Kostadinovic
4
and
Milena Santric Milicevic
2
;
5
Affiliations:
1
Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada
;
2
Faculty of Medicine, University of Belgrade, Belgrade, Serbia
;
3
Physical Medicine and Rehabilitation Department, University Children’s Hospital, Belgrade, Serbia
;
4
Clinical Center of Serbia, Belgrade, Serbia
;
5
Institute of Social Medicine, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
Keyword(s):
Pain Prediction, Logistic Regression.
Abstract:
Effective pain management can significantly improve quality of life and outcomes for various types of patients (e.g. elderly, adult, young). In order to improve our understanding of patients’ response to pain we need to develop adequate signal processing techniques that would enable us to understand underlying interdependencies. To this purpose in this paper we develop several different algorithms that can predict function related pain outcomes using a large database obtained as a part of the national health survey. As a part of the survey the respondents provided detailed information about general health care state, acute and chronic problems as well as personal perception of pain associated with performing two simple talks: walking on the flat surface and walking upstairs. We model the correspondent responses using parametric and non-parametric models and use health indicators (both chronic and acute) as explanatory variables. For the binomial model we propose parametric age depend
ent model and then compare its performance to the performance of the multinomial and histogram models.
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