Deep reinforcement learning for personalized treatment recommendation

Mingyang Liu, Xiaotong Shen, Wei Pan

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

In precision medicine, the ultimate goal is to recommend the most effective treatment to an individual patient based on patient-specific molecular and clinical profiles, possibly high-dimensional. To advance cancer treatment, large-scale screenings of cancer cell lines against chemical compounds have been performed to help better understand the relationship between genomic features and drug response; existing machine learning approaches use exclusively supervised learning, including penalized regression and recommender systems. However, it would be more efficient to apply reinforcement learning to sequentially learn as data accrue, including selecting the most promising therapy for a patient given individual molecular and clinical features and then collecting and learning from the corresponding data. In this article, we propose a novel personalized ranking system called Proximal Policy Optimization Ranking (PPORank), which ranks the drugs based on their predicted effects per cell line (or patient) in the framework of deep reinforcement learning (DRL). Modeled as a Markov decision process, the proposed method learns to recommend the most suitable drugs sequentially and continuously over time. As a proof-of-concept, we conduct experiments on two large-scale cancer cell line data sets in addition to simulated data. The results demonstrate that the proposed DRL-based PPORank outperforms the state-of-the-art competitors based on supervised learning. Taken together, we conclude that novel methods in the framework of DRL have great potential for precision medicine and should be further studied.

Original languageEnglish (US)
Pages (from-to)4034-4056
Number of pages23
JournalStatistics in Medicine
Volume41
Issue number20
DOIs
StatePublished - Sep 10 2022

Bibliographical note

Funding Information:
This research was partially supported by NSF Grant DMS‐1952539, NIH Grants R01AG069895, R01AG065636, R01AG074858, U01AG073079, and R01GM126002, and by the Minnesota Supercomputing Institute at the University of Minnesota. Open access funding enabled and organized by Projekt DEAL.

Publisher Copyright:
© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Keywords

  • Proximal Policy Optimization
  • actor-critic methods
  • deep learning
  • precision medicine
  • recommender systems

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