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Updated: Sep 7, 2025

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Deep reinforcement learning for personalized treatment recommendation.

Mingyang Liu1, Xiaotong Shen1, Wei Pan2

  • 1School of Statistics, University of Minnesota, Minneapolis, Minnesota, USA.

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|June 18, 2022
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Summary

This study introduces PPORank, a novel deep reinforcement learning (DRL) system for personalized cancer treatment recommendations. PPORank effectively ranks drugs for individual patients, outperforming traditional supervised learning methods.

Keywords:
Proximal Policy Optimizationactor-critic methodsdeep learningprecision medicinerecommender systems

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Area of Science:

  • Computational Biology
  • Artificial Intelligence in Medicine
  • Pharmacogenomics

Background:

  • Precision medicine aims to tailor treatments using individual patient data.
  • Current cancer drug screening relies on supervised learning, which can be inefficient.
  • Reinforcement learning offers a more adaptive approach for sequential data learning.

Purpose of the Study:

  • To develop a novel personalized ranking system for cancer drug recommendations.
  • To leverage deep reinforcement learning (DRL) for sequential treatment selection.
  • To improve the efficiency and effectiveness of precision medicine.

Main Methods:

  • Proposed Proximal Policy Optimization Ranking (PPORank), a DRL-based personalized ranking system.
  • Modeled drug recommendation as a Markov decision process.
  • Validated using large-scale cancer cell line datasets and simulated data.

Main Results:

  • PPORank demonstrated superior performance compared to state-of-the-art supervised learning competitors.
  • The DRL framework successfully learned to recommend suitable drugs sequentially.
  • Effectiveness shown in ranking drugs based on predicted effects per cell line/patient.

Conclusions:

  • Deep reinforcement learning (DRL) shows significant potential for advancing precision medicine.
  • PPORank offers a promising new direction for personalized cancer therapy.
  • Further research into DRL applications in precision medicine is warranted.