ReviewPediatric research2023
Using machine learning to impact on long-term clinical care: principles, challenges, and practicalities.
Review in Pediatric research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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
16 citing papers in PubMed, 41 citations in OpenAlex.
- From childhood to adulthood: an attempt to model long-term outcomes of ADHD using machine learning.European child & adolescent psychiatry · 2026Article
- Lipidomics in Melanoma: Insights into Disease Progression and Therapeutical Targets.International journal of molecular sciences · 2026Review
- Machine learning for predicting clinical outcomes of hospitalised children: a systematic review of applications in low- and middle-income countries.EClinicalMedicine · 2026Review
- The development of a data dictionary with clinical variables for artificial intelligence-driven tools in research on abdominal aortic aneurysms and peripheral arterial disease.European heart journal. Digital health · 2025Article
- Heterogeneity in Kawasaki disease patients with coronary artery abnormalities investigated by data-driven cluster analysis.Pediatric research · 2025Article
- Flexible imputation toolkit for electronic health records.Scientific reports · 2025Article
- Use of machine learning models for the prognostication of liver transplantation: A systematic review.World journal of transplantation · 2024Article
- Editorial: Artificial intelligence and machine learning in pediatric surgery.Frontiers in pediatrics · 2024Article
- Predictive analytics in bronchopulmonary dysplasia: past, present, and future.Frontiers in pediatrics · 2024Review
- Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications.Healthcare (Basel, Switzerland) · 2023Review
- Hyperparameter optimization for cardiovascular disease data-driven prognostic system.Visual computing for industry, biomedicine, and art · 2023Article
- Article
- The importance of high-quality 'big data' in the application of artificial intelligence in inflammatory bowel disease.Frontline gastroenterology · 2023Article
- Joint use of population pharmacokinetics and machine learning for optimizing antiepileptic treatment in pediatric population.Therapeutic advances in drug safety · 2023Article
- Article
- Progress and prospects for artificial intelligence in clinical practice: learning from COVID-19.Internal and emergency medicine · 2022Article
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 at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rise of machine learning in healthcare has significant implications for paediatrics. Long-term conditions with significant disease heterogeneity comprise large portions of the routine work performed by paediatricians. Improving outcomes through discovery of disease and treatment prediction models, alongside novel subgroup clustering of patients, are some of the areas in which machine learning holds significant promise. While artificial intelligence has percolated into routine use in our day to day lives through advertising algorithms, song or movie selections and sifting of spam emails, the ability of machine learning to utilise highly complex and dimensional data has not yet reached its full potential in healthcare. In this review article, we discuss some of the foundations of machine learning, including some of the basic algorithms. We emphasise the importance of correct utilisation of machine learning, including adequate data preparation and external validation. Using nutrition in preterm infants and paediatric inflammatory bowel disease as examples, we discuss the evidence and potential utility of machine learning in paediatrics. Finally, we review some of the future applications, alongside challenges and ethical considerations related to application of artificial intelligence. IMPACT: Machine learning is a widely used term; however, understanding of the process and application to healthcare is lacking. This article uses clinical examples to explore complex machine learning terms and algorithms. We discuss limitations and potential future applications within paediatrics and neonatal medicine.
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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.