Locomotor development prediction based on statistical model parameters identification.
This paper introduces an approach for parameters identification of a statistical predicting model with the use of the available individual data. Unknown parameters are separated into two groups: the ones specifying the average trend over large set of individuals and the ones describing the details of a concrete person. In order to calculate the vector of unknown parameters, a multidimensional constrained optimization problem is solved minimizing the discrepancy between real data and the model prediction over the set of feasible solutions. Both the individual retrospective data and factors influencing the individual dynamics are taken into account. The application of the method for predicting the movement of a patient with congenital motility disorders is considered.
The Center of Mathematical Modelling of Medicosocial Systems and Processes, The State National Research Politechnical University of Perm, Perm 614990, Russia. wildemann@mail.ru
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Wildemann
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Tashkinov
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Bronnikov
D000465:Algorithms; D002547:Cerebral Palsy / Q000534:rehabilitation*; D002648:Child; D002675:Child, Preschool; D006233:Persons with Disabilities; D006801:Humans; D007223:Infant; D008490:Medical Informatics / Q000379:methods*; D015233:Models, Statistical; D009048:Motor Skills / Q000502:physiology*; D012189:Retrospective Studies; D016896:Treatment Outcome