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Pavlovian Conditioned Approach Training in Rats

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Reinforcement Learning Methods in Public Health.

Justin Weltz¹, Alex Volfovsky¹, Eric B Laber²

¹Department of Statistical Science, Duke University, Durham, North Carolina.

Clinical Therapeutics

|January 21, 2022

Summary

Reinforcement learning (RL) offers optimal sequential decision-making for public health challenges. Applying RL can improve health outcomes and reduce resource use through efficient data-driven strategies.

Keywords:

decision making machine learning public health reinforcement learning

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Area of Science:

Machine learning
Artificial intelligence
Public health informatics

Background:

Reinforcement learning (RL) is a machine learning subfield for sequential decision-making under uncertainty.
RL optimizes cumulative utility by balancing experimentation costs and benefits.
It holistically evaluates actions for short-term and long-term stakeholder utility.

Purpose of the Study:

Introduce key concepts of Reinforcement Learning (RL).
Identify challenges and opportunities for RL in public health.
Review RL's potential for complex public health decision problems.

Main Methods:

Nontechnical review of RL theory and methodology.
Illustrative example of RL for infectious disease management.
Exploration of RL's application in public health decision-making.

Main Results:

RL can transform sequential decision problems in public health.
Optimal resource allocation via RL improves health outcomes.
RL enhances efficiency by reducing resource consumption.

Conclusions:

Public health decision-making can be optimized using RL.
RL enables efficient, data-driven, evidence-based strategies.
Consider RL for improved public health resource allocation and outcomes.