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

Research output: Contribution to journalConference articlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)157-189
Number of pages33
JournalProceedings of Machine Learning Research
Volume225
StatePublished - 2023
Event3rd Machine Learning for Health Symposium, ML4H 2023 - New Orleans, United States
Duration: Dec 10 2023 → …

Keywords

  • Adaptation
  • Differential equation
  • Disease progression
  • Reinforcement learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

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