ReviewPatterns (New York, N.Y.)2022
Breaking away from labels: The promise of self-supervised machine learning in intelligent health.
Review in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- AI-ECG classification for Brugada syndrome: A study of machine learning techniques to optimise for limited datasets.PLOS digital health · 2026Article
- Visual diagnostics for female genital schistosomiasis and the opportunity for improvement using computer vision.Parasitology · 2025Review
- Personalization of AI Using Personal Foundation Models Can Lead to More Precise Digital Therapeutics.JMIR AI · 2025Article
- Technical Note: Neural Network Architectures for Self-Supervised Body Part Regression Models with Automated Localized Segmentation Application.Journal of imaging informatics in medicine · 2025Article
- Digital twins as global learning health and disease models for preventive and personalized medicine.Genome medicine · 2025Review
- AI-Based Drug Discovery and Design for Different Genetic Designs.Methods in molecular biology (Clifton, N.J.) · 2025Article
- The first step is the hardest: pitfalls of representing and tokenizing temporal data for large language models.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- WISE: whole-scenario embryo identification using self-supervised learning encoder in IVF.Journal of assisted reproduction and genetics · 2024Article
- Use of Digitalisation and Machine Learning Techniques in Therapeutic Intervention at Early Ages: Supervised and Unsupervised Analysis.Children (Basel, Switzerland) · 2024Article
- Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection.IEEE journal of translational engineering in health and medicine · 2024Article
- Sequence-Type Classification of Brain MRI for Acute Stroke Using a Self-Supervised Machine Learning Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- Human-centred artificial intelligence for mobile health sensing: challenges and opportunities.Royal Society open science · 2023Review
- Self-Supervision for Medical Image Classification: State-of-the-Art Performance with ~100 Labeled Training Samples per Class.Bioengineering (Basel, Switzerland) · 2023Article
- Redesigning plant specialized metabolism with supervised machine learning using publicly available reactome data.Computational and structural biotechnology journal · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medicine is undergoing an unprecedented digital transformation, as massive amounts of health data are being produced, gathered, and curated, ranging from in-hospital (e.g., intensive care unit [ICU]) to person-generated data (wearables). Annotating all these data for training purposes in order to feed to deep learning models for pattern recognition is impractical. Here, we discuss some exciting recent results of self-supervised learning (SSL) applications to high-resolution health signals. These examples leverage unlabeled data to learn meaningful representations that can generalize to situations where the ground truth is inadequate or simply infeasible to collect due to the high burden or associated costs. The most prominent bottleneck of deep learning today is access to labeled, carefully curated datasets, and self-supervision on health signals opens up new possibilities to eliminate data silos through general-purpose models that can transfer to low-resource environments and tasks.
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.