ReviewAnnals of biomedical engineering2024
A Review of Machine Learning Algorithms for Biomedical Applications.
Review in Annals of biomedical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 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
60 citing papers in PubMed.
- Machine learning prediction of CKD progression in hyperglycemic elderly adults: a prospective community cohort study.Renal failure · 2026Article
- A review of FDG-PET biomarker analysis methods in Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Machine Learning Classification of Physiological Dynamics During Standardized Task-Demand Transitions.Bioengineering (Basel, Switzerland) · 2026Article
- Label-free Raman spectroscopic imaging enables high-content molecular phenotyping of respiratory diseases from bronchoalveolar lavage fluid.Journal of translational medicine · 2026Article
- Review of image segmentation techniques for biomedical micro-CT: from laboratory absorption imaging to synchrotron phase-contrast.Npj imaging · 2026Review
- Machine learning-driven cancer diagnostics with improved robustness and interpretability.Chemical science · 2026Review
- Explainable machine learning for early prediction and anatomical classification of pulmonary embolism in the emergency department.BMC emergency medicine · 2026Article
- Single-cell analysis reveals that NRG1/3-ERBB4 signaling affects metabolic reprogramming and immune escape in Wilms tumor.Biology direct · 2026Article
- Identification of GCNT3 as a glycometabolism-associated biomarker in endometrial cancer.Scientific reports · 2026Article
- Article
- Supervised Learning for Collagen Segmentation in Bright-Field Histology: A Comparative Evaluation of U-Net and MLP.Annals of biomedical engineering · 2026Article
- Machine learning-enabled diagnosis of viral respiratory infections from exhaled volatile organic compound analysis.BMC pulmonary medicine · 2026Article
- Review
- Overview in Machine-Learning-Assisted Sensing Techniques for Monitoring COVID-19.Micromachines · 2026Review
- A clinically interpretable prediction model for acute mortality in patients with pneumonia requiring mechanical ventilation.Respiratory research · 2026Article
- Supragingival Biomarker flora of Children With and Without Cariogenic Disease and Black Stains, Aged 3 to 6 Years.International dental journal · 2026Article
- Features Associated with Therapy Switch Among PPD CorEvitas Psoriasis Registry Patients.Dermatology and therapy · 2026Article
- A Review of High-Throughput Optical Sensors for Food Detection Based on Machine Learning.Foods (Basel, Switzerland) · 2026Review
- Automatic uncertainty evaluation for determining the number of components in nested models and the shrinkage regularization parameters.PloS one · 2026Article
- Single-Cell and Multi-Omics-Based Characterization of Gastric Cancer Identifies TPP1 as a Potential Target for Gastric Cancer Progression and Treatment.Oncology research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As the amount and complexity of biomedical data continue to increase, machine learning methods are becoming a popular tool in creating prediction models for the underlying biomedical processes. Although all machine learning methods aim to fit models to data, the methodologies used can vary greatly and may seem daunting at first. A comprehensive review of various machine learning algorithms per biomedical applications is presented. The key concepts of machine learning are supervised and unsupervised learning, feature selection, and evaluation metrics. Technical insights on the major machine learning methods such as decision trees, random forests, support vector machines, and k-nearest neighbors are analyzed. Next, the dimensionality reduction methods like principal component analysis and t-distributed stochastic neighbor embedding methods, and their applications in biomedical data analysis were reviewed. Moreover, in biomedical applications predominantly feedforward neural networks, convolutional neural networks, and recurrent neural networks are utilized. In addition, the identification of emerging directions in machine learning methodology will serve as a useful reference for individuals involved in biomedical research, clinical practice, and related professions who are interested in understanding and applying machine learning algorithms in their research or practice.
Indexed as
Identifiers
38383870What 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.