ReviewFrontiers in oncology2023
Common statistical concepts in the supervised Machine Learning arena.
Review in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Use of Wearable Sensors and Machine Learning Methods to Estimate Biomechanical Characteristics During Standing Posture or Locomotion: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Artificial Intelligence for Personalized Prediction of Post-TIPS Outcomes: Integrating Clinical, Biochemical, and Radiomics Data-A Narrative Review.Journal of clinical medicine · 2026Review
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
- Bridging Traditional Modeling and Artificial Intelligence in Measles Epidemiology: Methods, Applications, and Future Directions-A Narrative Review.Journal of clinical medicine · 2026Review
- Cracking the Code: Computational Image Analysis Tools for Histopathological and Morphometric Insights.Journal of imaging · 2026Review
- Article
- Integrated Anthropometric, Physiological and Biological Assessment of Elite Youth Football Players Using Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- A Computer Vision and Machine Learning Approach to Classify Views in Distal Radius Radiographs.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2025Article
- Machine learning-assisted screening of clinical features for predicting difficult-to-treat rheumatoid arthritis.Scientific reports · 2025Article
- Interpretable machine learning models for prolonged Emergency Department wait time prediction.BMC health services research · 2025Article
- Forecasting mental states in schizophrenia using digital phenotyping data.PLOS digital health · 2025Article
- Artificial intelligence in pediatric allergy research.European journal of pediatrics · 2024Review
- Laboratory Preparation for Digital Medicine in Healthcare 4.0: An Investigation Into the Awareness and Applications of Big Data and Artificial Intelligence.Annals of laboratory medicine · 2024Article
- AI-smartphone markerless motion capturing of hip, knee, and ankle joint kinematics during countermovement jumps.European journal of sport science · 2024Article
- Intelligent salivary biosensors for periodontitis: in vitro simulation of oral oxidative stress conditions.Medical & biological engineering & computing · 2024Article
- An Ensemble Classifiers for Improved Prediction of Native-Non-Native Protein-Protein Interaction.International journal of molecular sciences · 2024Article
- Image Reconstruction Using Supervised Learning in Wearable Electrical Impedance Tomography of the Thorax.Sensors (Basel, Switzerland) · 2023Article
- A microfluidic approach for label-free identification of small-sized microplastics in seawater.Scientific reports · 2023Article
- The ChatGPT conundrum: Human-generated scientific manuscripts misidentified as AI creations by AI text detection tool.Journal of pathology informatics · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the core elements of Machine Learning (ML) is statistics and its embedded foundational rules and without its appropriate integration, ML as we know would not exist. Various aspects of ML platforms are based on statistical rules and most notably the end results of the ML model performance cannot be objectively assessed without appropriate statistical measurements. The scope of statistics within the ML realm is rather broad and cannot be adequately covered in a single review article. Therefore, here we will mainly focus on the common statistical concepts that pertain to supervised ML (i.e. classification and regression) along with their interdependencies and certain limitations.
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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.