ArticleWorld journal of gastroenterology2022
Machine learning models and over-fitting considerations.
Article in World journal of gastroenterology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 4 of them syntheses that pooled it.
What it found
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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
79 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Machine learning in the prediction of diabetic peripheral neuropathy: a systematic review.BMC medical informatics and decision making · 2025Pooled it
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- The value of machine learning approaches in the diagnosis of early gastric cancer: a systematic review and meta-analysis.World journal of surgical oncology · 2024Pooled it
- Current state of radiomics in pediatric neuro-oncology practice: a systematic review.Pediatric radiology · 2023Pooled it
- Surveillance for Asiatic filoviruses and zoonotic paramyxoviruses at a perceived wildlife-human interface.Emerging microbes & infections · 2026Article
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- A data-driven approach to define the stages and patterns of neurological recovery after traumatic spinal cord injury.Journal of neurology · 2026Article
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- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Impact of RAASIs on Potassium and Mortality in a Large Cohort of Hemodialysis Patients: Practical Excursus and Comparison Between Traditional Statistics and Machine Learning.Journal of clinical medicine · 2026Article
- Predicting mortality risk in hospitalized ACS patients with hypertensive comorbidity: an interpretable machine learning approach.BMC medical informatics and decision making · 2026Article
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- Development of a machine-learning based diagnosis procedure to distinguish aortic dissection from non-ST-elevation myocardial infarction.BMC medical informatics and decision making · 2026Article
- Short-Term Arrhythmia Prediction Using AI Based on Daily Data From Implantable Devices: Multicenter Prospective Observational Study.JMIR cardio · 2026Observational
- Development and validation of a cardiometabolic multimorbidity prediction model in middle-aged and older adults.Scientific reports · 2026Article
- Hybrid generative-ensemble approach for predicting recycled aggregate concrete strength properties.Scientific reports · 2026Article
- Multistage machine learning model for automated referral triage in pain medicine.Future healthcare journal · 2026Article
- Harnessing artificial intelligence for genomic variant prediction: advances, challenges, and future directions.GigaScience · 2026Review
- Association between the oxidative balance score and all-cause mortality in patients with cardiovascular disease-cancer comorbidity.Journal of health, population, and nutrition · 2026Article
19 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning models may outperform traditional statistical regression algorithms for predicting clinical outcomes. Proper validation of building such models and tuning their underlying algorithms is necessary to avoid over-fitting and poor generalizability, which smaller datasets can be more prone to. In an effort to educate readers interested in artificial intelligence and model-building based on machine-learning algorithms, we outline important details on cross-validation techniques that can enhance the performance and generalizability of such models.
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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.