Data-Driven Management of Post-Transplant Medications
2020
Organ-transplanted patients typically receive high amounts of immunosuppressive drugs as a mechanism to reduce their risk of organ rejection. However, because of the diabetogenic effect of these drugs, this practice exposes them to a greater risk of new-onset diabetes after transplantation (NODAT), and hence, becoming insulin dependent. This study develops effective medication management strategies to address the common conundrum of balancing the risk of organ rejection versus that of NODAT. The article presents a robust stochastic decision-making framework that allows for incorporating (1) false-positive and false-negative errors of medical tests, (2) inevitable estimation errors when data sets are used, (3) variability among physician’ attitudes toward ambiguous outcomes, and (4) dynamic and patient risk-profile-dependent progression of health conditions.
Data-Driven Management of Post-Transplant Medications
Source:
Boloori A, Saghafian S, Chakkera HA, Cook CB. Data-Driven Management of Post-Transplant Medications: An Ambiguous Partially Observable Markov Decision Process Approach. Manufacturing and Service Operations Management (M&SOM) 2020; 22 (5): 1066-1087. https://doi.org/10.1287/msom.2019.0797