REMEDI: REinforcement learning-driven adaptive MEtabolism modeling of
  primary sclerosing cholangitis DIsease progression
Chang Hu Krishnakant V. Saboo Ahmad H. Ali Brian D. Juran Konstantinos N. Lazaridis Ravishankar K. Iyer
Primary sclerosing cholangitis (PSC) is a rare disease wherein altered bile
acid metabolism contributes to sustained liver injury. This paper introduces
REMEDI, a framework that captures bile acid dynamics and the body's adaptive
response during PSC progression that can assist in exploring treatments. REMEDI
merges a differential equation (DE)-based mechanistic model that describes bile
acid metabolism with reinforcement learning (RL) to emulate the body's
adaptations to PSC continuously. An objective of adaptation is to maintain
homeostasis by regulating enzymes involved in bile acid metabolism. These
enzymes correspond to the parameters of the DEs. REMEDI leverages RL to
approximate adaptations in PSC, treating homeostasis as a reward signal and the
adjustment of the DE parameters as the corresponding actions. On real-world
data, REMEDI generated bile acid dynamics and parameter adjustments consistent
with published findings. Also, our results support discussions in the
literature that early administration of drugs that suppress bile acid synthesis
may be effective in PSC treatment.