ArticleMachine learning2024
Deep reinforcement learning for multi-class imbalanced training: applications in healthcare.
Article in Machine learning, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed.
- Strategies for Class-Imbalanced Learning in Multi-Sensor Medical Imaging.Sensors (Basel, Switzerland) · 2026Review
- A smart community interactive art therapy platform based on multimodal computer graphics and resilient artificial intelligence for home-based elderly care.Scientific reports · 2025Article
- Article
- A hybrid reinforcement learning and knowledge graph framework for financial risk optimization in healthcare systems.Scientific reports · 2025Article
- Weighted 2D-kernel density estimations provide a new probabilistic measure for epigenetic age.Genome biology · 2025Article
- Hybrid clustering strategies for effective oversampling and undersampling in multiclass classification.Scientific reports · 2025Article
- Generalizability assessment of AI models across hospitals in a low-middle and high income country.Nature communications · 2024Article
- Addressing label noise for electronic health records: insights from computer vision for tabular data.BMC medical informatics and decision making · 2024Article
- Mitigating machine learning bias between high income and low-middle income countries for enhanced model fairness and generalizability.Scientific reports · 2024Article
- Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification.European heart journal. Digital health · 2024Article
- An adversarial training framework for mitigating algorithmic biases in clinical machine learning.NPJ digital medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
With the rapid growth of memory and computing power, datasets are becoming increasingly complex and imbalanced. This is especially severe in the context of clinical data, where there may be one rare event for many cases in the majority class. We introduce an imbalanced classification framework, based on reinforcement learning, for training extremely imbalanced data sets, and extend it for use in multi-class settings. We combine dueling and double deep Q-learning architectures, and formulate a custom reward function and episode-training procedure, specifically with the capability of handling multi-class imbalanced training. Using real-world clinical case studies, we demonstrate that our proposed framework outperforms current state-of-the-art imbalanced learning methods, achieving more fair and balanced classification, while also significantly improving the prediction of minority classes. Supplementary Information: The online version contains supplementary material available at 10.1007/s10994-023-06481-z.
Indexed as
Identifiers
What OpenQuestion holds
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.