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Urmila Diwekar

University of Illinois at Chicago, USA

Title: A Decision Support Tool for Optimized Personalized Medicine for In vitro Fertilization

Biography

Biography: Urmila Diwekar

Abstract

Superovulation is a drug-induced method to enable multiple ovulation per menstrual cycle and key component towards a successful IVF cycle. Although there are the general guidelines for dosage, the dose is not optimized for each patient, and complications, such as overstimulation, can occur. To overcome the shortcomings of this general system, a mathematical procedure and a decision support tool is developed which can provide a customized model of this stage regarding the size distribution of follicles obtained per cycle as a function of the chemical interactions of the drugs used and the conditions imposed on the patient during the cycle.  Uncertainty and risk are modeled and included in optimal drug dosage decisions. This paper describes the theory, model, the optimal control procedure, and the decision support tool for improving outcomes of IVF treatment for all the four protocols used in real practice. The validation of the procedure is performed using clinical data from more than 100 patients previously undergone IVF cycles. Customized patient-specific model parameters are obtained by using initial two-day data for each patient and validated. The models are then used for predicting the customized optimal drug dosage for each patient. Two clinical trials were conducted in India. The results from the trials show that the dosage predicted by using the model is 40% less than the suggestion made by the IVF clinicians. The testing and monitoring requirements for patients using optimized drug dosage is reduced by 72%.