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closePersonalization and patient-specific modeling
Posted by barneao on 27 Jul 2014 at 08:08 GMT
A problem we often see in the personalization of models is not enough measured data on one side and too many variables on the other side. Too many variables to fit create a problem of overfitting similar to a high degree polynomial used in curvefitting. I a similar paper on the same subject using quite similar techniques, published last year in CVET "Inverse Solution of the Fetal-Circulation Model Based on Ultrasound Doppler Measurements" by Luria et al. (and myself), we chose to minimize the number parameters based on the physics and mechanical properties that are common to a group of elements in the model and also impose the physiology and pathophysiology knowledge to group parameters. The problem of minimizing the number of parameters is very important to allow the deduction of clinically relevant information from the patient-specific model and the choice is not easy. In the above mentioned paper we have made different decisions regarding this problem. In this paper a number of values were used based on allometric expressions that are based on statistics thereby limiting the personalization of the model. This is indeed a difficult problem and therefore it may require more than three patients to assess clinical usefulness of the model.