# Reinforcement Learning based Disease Progression Model for Alzheimer's Disease

arXiv: https://arxiv.org/abs/2106.16187 · HTML: https://mlsanity.com/paper/2106.16187/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 48 references that are in the map.
- First posted on arXiv 30 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data](https://mlsanity.com/paper/2001.09016/) (2020)
- [The multiphysics of prion-like diseases: progression and atrophy](https://mlsanity.com/paper/1804.01958/) (2018)
- [Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer's disease severity](https://mlsanity.com/paper/1812.04994/) (2018)
- [Reinforcement Learning Framework for Deep Brain Stimulation Study](https://mlsanity.com/paper/2002.10948/) (2020)

## Built on by (most cited first, 10 of 10)

- [Longformer: Longitudinal Transformer for Alzheimer's Disease Classification with Structural MRIs](https://mlsanity.com/paper/2302.00901/) (2023)
- [Align Your Intents: Offline Imitation Learning via Optimal Transport](https://mlsanity.com/paper/2402.13037/) (2024)
- [Beyond Prediction: Reinforcement Learning as the Defining Leap in Healthcare AI](https://mlsanity.com/paper/2508.21101/) (2025)
- [Alzheimers Disease Diagnosis using Machine Learning: A Review](https://mlsanity.com/paper/2304.09178/) (2023)
- [Adaptive Aggregation for Safety-Critical Control](https://mlsanity.com/paper/2302.03586/) (2023)
- [REMEDI: REinforcement learning-driven adaptive MEtabolism modeling of primary sclerosing cholangitis DIsease progression](https://mlsanity.com/paper/2310.01426/) (2023)
- [Unifying Interpretability and Explainability for Alzheimer's Disease Progression Prediction](https://mlsanity.com/paper/2406.07777/) (2024)
- [Uncertainty Quantification in Alzheimer's Disease Progression Modeling](https://mlsanity.com/paper/2408.14478/) (2024)
- [LearnAD: Learning Interpretable Rules for Brain Networks in Alzheimer's Disease Classification](https://mlsanity.com/paper/2601.00877/) (2025)
- [ALPACA: A Reinforcement Learning Environment for Medication Repurposing and Treatment Optimization in Alzheimer's Disease](https://mlsanity.com/paper/2602.19298/) (2026)

## Abstract (arXiv)

We model Alzheimer's disease (AD) progression by combining differential equations (DEs) and reinforcement learning (RL) with domain knowledge. DEs provide relationships between some, but not all, factors relevant to AD. We assume that the missing relationships must satisfy general criteria about the working of the brain, for e.g., maximizing cognition while minimizing the cost of supporting cognition. This allows us to extract the missing relationships by using RL to optimize an objective (reward) function that captures the above criteria. We use our model consisting of DEs (as a simulator) and the trained RL agent to predict individualized 10-year AD progression using baseline (year 0) features on synthetic and real data. The model was comparable or better at predicting 10-year cognition trajectories than state-of-the-art learning-based models. Our interpretable model demonstrated, and provided insights into, "recovery/compensatory" processes that mitigate the effect of AD, even though those processes were not explicitly encoded in the model. Our framework combines DEs with RL for modelling AD progression and has broad applicability for understanding other neurological disorders.