ArticleStatistics in medicine2022
Deep reinforcement learning for personalized treatment recommendation.
Article in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review.Frontiers in digital health · 2025Pooled it
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- The impact of deep learning and omics data in transforming precision therapy for brain cancer.Frontiers in bioinformatics · 2026Review
- Advancing genome-based precision medicine: a review on machine learning applications for rare genetic disorders.Briefings in bioinformatics · 2025Review
- Role of Artificial Intelligence and Personalized Medicine in Enhancing HIV Management and Treatment Outcomes.Life (Basel, Switzerland) · 2025Review
- Reinforcement Learning in Personalized Medicine: A Comprehensive Review of Treatment Optimization Strategies.Cureus · 2025Review
- From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care.Biomedicines · 2025Review
- Navigating disruption in the PID landscape: embracing opportunities and anticipating threats in the next ten years.Frontiers in immunology · 2025Review
- Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.Frontiers in digital health · 2025Review
- Metabolic modelling as a powerful tool to identify critical components of Pneumocystis growth medium.PLoS computational biology · 2024Article
- Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions.Current oncology (Toronto, Ont.) · 2024Review
- Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer.Briefings in bioinformatics · 2024Review
- Reliable anti-cancer drug sensitivity prediction and prioritization.Scientific reports · 2024Article
- A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction.Briefings in bioinformatics · 2024Article
- Building digital patient pathways for the management and treatment of multiple sclerosis.Frontiers in immunology · 2024Review
- Optimized models and deep learning methods for drug response prediction in cancer treatments: a review.PeerJ. Computer science · 2024Article
- A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future.Aging clinical and experimental research · 2023Review
- Development and validation of a reinforcement learning model for ventilation control during emergence from general anesthesia.NPJ digital medicine · 2023Article
- Bottom-up and top-down paradigms of artificial intelligence research approaches to healthcare data science using growing real-world big data.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Deep learning methods for drug response prediction in cancer: Predominant and emerging trends.Frontiers in medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.