BEGIN:VCALENDAR
PRODID:-//eluceo/ical//2.0/EN
VERSION:2.0
CALSCALE:GREGORIAN
BEGIN:VEVENT
UID:2ac2d5adf91d45ecad27118d1e548193
DTSTAMP:20240501T224553Z
SUMMARY:Valuing Prearranged Paired Kidney Exchanges: A Stochastic Game Appr
 oach
DESCRIPTION: \n\nAbstract: End-stage renal disease (ESRD) is the ninth-lead
 ing cause of\ndeath in the U.S. Transplantation is the most viable therapy
  for ESRD\npatients\, but there is a severe disparity between the demand f
 or\nkidneys for transplantation and the supply. This shortage is further\n
 complicated by incompatibilities in blood-type and antigen matching\nbetwe
 en patient-donor pairs. Paired kidney exchange (PKE)\, a\ncross-exchange o
 f kidneys among incompatible patient-donor pairs\,\novercomes many difficu
 lties in matching patients with incompatible\ndonors. PKEs have grown rapi
 dly over the last two decades. In a PKE\,\ntransplantation surgeries take 
 place simultaneously\, so that no donor\nmay renege after his/her intended
  recipient receives the organ.\nAlthough others have modeled PKEs\, we are
  the first to consider\npatient autonomy and the timing of the exchange. A
 s current PKE\npractice aims to maximize only the number of transplants\, 
 the question\nof determining a more accurate value of a match remains uncl
 ear.\n\nWe consider a cyclic PKE with an arbitrary number of patients and\
 nconstruct life-expectancy-based edge weights under patient autonomy.\nBec
 ause the patients’ health statuses are dynamic\, and\ntransplantation su
 rgeries require compatibility between the\npatients’ willingnesses to ex
 change\, we model the patients’\ntransplant timing decisions as a stocha
 stic game in which each patient\naims to maximize his/her life expectancy.
  We explore necessary and\nsufficient conditions for patients’ decisions
  to be a Nash\nequilibrium\, and formulate a mixed-integer linear programm
 ing\nrepresentation of equilibrium constraints\, which provides a\ncharact
 erization of the socially optimal equilibria. We empirically\nconfirm that
  randomized strategies do not yield a social welfare gain\nover pure strat
 egies. We also quantify the social welfare loss due to\npatient autonomy a
 nd demonstrate that maximizing the number of\ntransplants may be undesirab
 le. Our results highlight the importance\nof the timing of an exchange and
  disease severity on matching\npatient-donor pairs.\n\nBrief Bio: Murat Ku
 rt is a Senior Scientist of Outcomes Research at\nMerck & Co. He earned hi
 s PhD in Industrial Engineering at the\nUniversity of Pittsburgh and serve
 d as a faculty in Industrial and\nSystems Engineering at the University at
  Buffalo prior to joining\nMerck. His research interests involve the imple
 mentation of Markov\ndecision processes and mathematical programming on me
 dical decision\nmaking\, scheduling and service operations problems. His r
 esearch in\napplied health sciences span varying contexts from treatment p
 lanning\nfor Type 2 diabetes and colorectal cancer patients to the timing 
 of\nkidney exchanges for end-stage renal disease patients. His research on
 \nhealthcare applications has been recognized by the IIE’s Pritsker\nDoc
 toral Dissertation Prize and INFORMS Service Science Section’s\nBest Pap
 er Awards.\n 
DTSTART:20151123T180000Z
DTEND:20151123T190000Z
LOCATION:Gerri C. LeBow Hall\, 3220 Market Street\, 722\, Philadelphia\, PA
  19104
END:VEVENT
END:VCALENDAR