# Innovations in Integrating Machine Learning and Agent-Based Modeling of Biomedical Systems

arXiv: https://arxiv.org/abs/2206.01092 · HTML: https://mlsanity.com/paper/2206.01092/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 196 references that are in the map.
- First posted on arXiv 2 Jun 2022.

## Builds on (most cited first, 11 of 11)

- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems](https://mlsanity.com/paper/1903.12394/) (2019)
- [Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning](https://mlsanity.com/paper/1811.12808/) (2018)
- [Drug discovery with explainable artificial intelligence](https://mlsanity.com/paper/2007.00523/) (2020)
- [Causal Machine Learning for Healthcare and Precision Medicine](https://mlsanity.com/paper/2205.11402/) (2022)
- [Learning to Perform Physics Experiments via Deep Reinforcement Learning](https://mlsanity.com/paper/1611.01843/) (2016)
- [Deep Reinforcement Learning using Genetic Algorithm for Parameter Optimization](https://mlsanity.com/paper/1905.04100/) (2019)
- [Deep Reinforcement Learning of Cell Movement in the Early Stage of C. elegans Embryogenesis](https://mlsanity.com/paper/1801.04600/) (2018)
- [Visual Analytics of Image-Centric Cohort Studies in Epidemiology](https://mlsanity.com/paper/1501.04009/) (2015)
- [Agent-Based Modelling: An Overview with Application to Disease Dynamics](https://mlsanity.com/paper/2007.04192/) (2020)
- [Automatic Generation of Individual Fuzzy Cognitive Maps from Longitudinal Data](https://mlsanity.com/paper/2202.07065/) (2022)

## Built on by (most cited first, 4 of 4)

- [SDGym: Low-Code Reinforcement Learning Environments using System Dynamics Models](https://mlsanity.com/paper/2310.12494/) (2023)
- [Promising and worth-to-try future directions for advancing state-of-the-art surrogates methods of agent-based models in social and health computational sciences](https://mlsanity.com/paper/2403.04417/) (2024)
- [Quantum Circuit Simulation of Compartmental Drug Dynamics: Leveraging Variational Algorithms for Nonlinear Mixed-Effects Population Pharmacokinetics](https://mlsanity.com/paper/2605.09691/) (2026)
- [Machine learning-assisted calibration of Agent-based Models: surrogate-based optimization with Genetic Algorithm and Particle Swarm Optimization](https://mlsanity.com/paper/2609.13247/) (2026)

## Abstract (arXiv)

Agent-based modeling (ABM) is a well-established paradigm for simulating complex systems via interactions between constituent entities. Machine learning (ML) refers to approaches whereby statistical algorithms 'learn' from data on their own, without imposing a priori theories of system behavior. Biological systems -- from molecules, to cells, to entire organisms -- consist of vast numbers of entities, governed by complex webs of interactions that span many spatiotemporal scales and exhibit nonlinearity, stochasticity and intricate coupling between entities. The macroscopic properties and collective dynamics of such systems are difficult to capture via continuum modelling and mean-field formalisms. ABM takes a 'bottom-up' approach that obviates these difficulties by enabling one to easily propose and test a set of well-defined 'rules' to be applied to the individual entities (agents) in a system. Evaluating a system and propagating its state over discrete time-steps effectively simulates the system, allowing observables to be computed and system properties to be analyzed. Because the rules that govern an ABM can be difficult to abstract and formulate from experimental data, there is an opportunity to use ML to help infer optimal, system-specific ABM rules. Once such rule-sets are devised, ABM calculations can generate a wealth of data, and ML can be applied there too -- e.g., to probe statistical measures that meaningfully describe a system's stochastic properties. As an example of synergy in the other direction (from ABM to ML), ABM simulations can generate realistic datasets for training ML algorithms (e.g., for regularization, to mitigate overfitting). In these ways, one can envision various synergistic ABMrightleftharpoonsML loops. This review summarizes how ABM and ML have been integrated in contexts that span spatiotemporal scales, from cellular to population-level epidemiology.