On the public citation graph

Utility of machine learning in developing a predictive model for early-age-onset colorectal neoplasia using electronic health records

https://doi.org/10.1371/journal.pone.0265209
0 signed · 67 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1001/jamanetworkopen.2019.20407openalex-snapshot
Trends in Incidence of Early-Onset Colorectal Cancer in the United States Among Those Approaching Screening Age
inferredcites →https://doi.org/10.1001/jamaoncol.2018.1901openalex-snapshot
Cost-effectiveness and Benefit-to-Harm Ratio of Risk-Stratified Screening for Breast Cancer
inferredcites →https://doi.org/10.1001/jamasurg.2014.1756openalex-snapshot
Increasing Disparities in the Age-Related Incidences of Colon and Rectal Cancers in the United States, 1975-2010
inferredcites →https://doi.org/10.1001/jamasurg.2014.3572openalex-snapshot
Overtreatment of Young Adults With Colon Cancer
inferredcites →https://doi.org/10.1007/978-0-387-09823-4_45openalex-snapshot
Data Mining for Imbalanced Datasets: An Overview
inferredcites →https://doi.org/10.1007/978-0-387-84858-7_10openalex-snapshot
Boosting and Additive Trees
inferredcites →https://doi.org/10.1007/978-1-4614-6849-3openalex-snapshot
Applied Predictive Modeling
inferredcites →https://doi.org/10.1007/s10462-009-9124-7openalex-snapshot
Ensemble-based classifiers
inferredcites →https://doi.org/10.1007/s12178-020-09600-8openalex-snapshot
Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions
inferredcites →https://doi.org/10.1016/j.amjcard.2008.03.079openalex-snapshot
Comparison of Serum Lipid Values in Subjects With and Without the Metabolic Syndrome
inferredcites →https://doi.org/10.1016/j.artmed.2010.05.002openalex-snapshot
Missing data imputation using statistical and machine learning methods in a real breast cancer problem
inferredcites →https://doi.org/10.1016/j.cgh.2019.10.009openalex-snapshot
Risk Factors Associated With Early-Onset Colorectal Cancer
inferredcites →https://doi.org/10.1016/j.cgh.2020.08.062openalex-snapshot
Diagnostic Yield of Colonoscopy in Patients With Symptoms Compatible With Rome IV Functional Bowel Disorders
inferredcites →https://doi.org/10.1016/j.gie.2014.07.058openalex-snapshot
Quality indicators for colonoscopy
inferredcites →https://doi.org/10.1016/s1470-2045(13)70216-xopenalex-snapshot
Assessment of volumetric growth rates of small colorectal polyps with CT colonography: a longitudinal study of natural history
inferredcites →https://doi.org/10.1038/bjc.2014.304openalex-snapshot
Risk factors for delay in symptomatic presentation: a survey of cancer patients
inferredcites →https://doi.org/10.1038/s41395-018-0256-8openalex-snapshot
Burden of Gastrointestinal Symptoms in the United States: Results of a Nationally Representative Survey of Over 71,000 Americans
inferredcites →https://doi.org/10.1038/s41551-018-0195-0openalex-snapshot
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
inferredcites →https://doi.org/10.1038/srep45938openalex-snapshot
Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer
inferredcites →https://doi.org/10.1053/j.gastro.2015.09.041openalex-snapshot
Quantification of Adequate Bowel Preparation for Screening or Surveillance Colonoscopy in Men
inferredcites →https://doi.org/10.1053/j.gastro.2016.08.053openalex-snapshot
Recommendations on Fecal Immunochemical Testing to Screen for Colorectal Neoplasia: A Consensus Statement by the US Multi-Society Task Force on Colorectal Cancer
inferredcites →https://doi.org/10.1053/j.gastro.2019.03.023openalex-snapshot
Cost-Effectiveness and National Effects of Initiating Colorectal Cancer Screening for Average-Risk Persons at Age 45 Years Instead of 50 Years
inferredcites →https://doi.org/10.1053/j.gastro.2019.07.055openalex-snapshot
Epidemiology and Mechanisms of the Increasing Incidence of Colon and Rectal Cancers in Young Adults
inferredcites →https://doi.org/10.1053/j.gastro.2020.12.009openalex-snapshot
A High Percentage of Early-age Onset Colorectal Cancer Is Potentially Preventable
inferredcites →https://doi.org/10.1073/pnas.79.8.2554openalex-snapshot
Neural networks and physical systems with emergent collective computational abilities.
inferredcites →https://doi.org/10.1093/aje/kwr188openalex-snapshot
Sojourn Time of Preclinical Colorectal Cancer by Sex and Age: Estimates From the German National Screening Colonoscopy Database
inferredcites →https://doi.org/10.1093/jnci/djac003openalex-snapshot
Risk Stratification for Early-Onset Colorectal Cancer Using a Combination of Genetic and Environmental Risk Scores: An International Multi-Center Study
inferredcites →https://doi.org/10.1093/jnci/djw322openalex-snapshot
Colorectal Cancer Incidence Patterns in the United States, 1974–2013
inferredcites →https://doi.org/10.1097/md.0b013e3181881354openalex-snapshot
Young-Onset Colorectal Cancer in Patients With No Known Genetic Predisposition
inferredcites →https://doi.org/10.1098/rsif.2017.0387openalex-snapshot
Opportunities and obstacles for deep learning in biology and medicine
inferredcites →https://doi.org/10.1109/icdar.1995openalex-snapshot
Proceedings of 3rd International Conference on Document Analysis and Recognition
inferredcites →https://doi.org/10.1109/jbhi.2016.2636665openalex-snapshot
Deep Learning for Health Informatics
inferredcites →https://doi.org/10.1136/gutjnl-2019-319511openalex-snapshot
Global patterns and trends in colorectal cancer incidence in young adults
inferredcites →https://doi.org/10.1136/gutjnl-2020-321698openalex-snapshot
Derivation and Validation of a Predictive Model for Advanced Colorectal Neoplasia in Asymptomatic Adults
inferredcites →https://doi.org/10.1136/qshc.2009.033712openalex-snapshot
Reducing referral delays in colorectal cancer diagnosis: is it about how you ask?
inferredcites →https://doi.org/10.1148/radiol.2017170236openalex-snapshot
Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs
inferredcites →https://doi.org/10.1186/s13058-019-1158-4openalex-snapshot
Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models
inferredcites →https://doi.org/10.1200/jco.2008.17.4813openalex-snapshot
Validation of a Colorectal Cancer Risk Prediction Model Among White Patients Age 50 Years and Older
inferredcites →https://doi.org/10.1371/journal.pone.0221421openalex-snapshot
Scoring colorectal cancer risk with an artificial neural network based on self-reportable personal health data
inferredcites →https://doi.org/10.14309/ajg.0000000000000370openalex-snapshot
Head-to-Head Comparison of the Performance of 17 Risk Models for Predicting Presence of Advanced Neoplasms in Colorectal Cancer Screening
inferredcites →https://doi.org/10.1613/jair.614openalex-snapshot
Popular Ensemble Methods: An Empirical Study
inferredcites →https://doi.org/10.2307/1267865openalex-snapshot
Applied Multivariate Statistical Analysis
inferredcites →https://doi.org/10.3322/caac.21457openalex-snapshot
Colorectal cancer screening for average‐risk adults: 2018 guideline update from the American Cancer Society
inferredcites →https://doi.org/10.3748/wjg.14.6536openalex-snapshot
Colonoscopic evaluation of minimal rectal bleeding in average-risk patients for colorectal cancer
inferredcites →https://doi.org/10.3748/wjg.v24.i34.3919openalex-snapshot
Predicting the presence of adenomatous polyps during colonoscopy with National Cancer Institute Colorectal Cancer Risk-Assessment Tool
inferredcites →https://doi.org/10.7326/0003-4819-139-10-200311180-00007openalex-snapshot
Use of Metabolic Markers To Identify Overweight Individuals Who Are Insulin Resistant
inferredcites →https://doi.org/10.7326/m14-0697openalex-snapshot
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4157245openalex-extracted
Predicting Effectiveness of Drug from Patient’s Review
inferred← citeshttps://doi.org/10.1007/978-3-031-40295-1_13openalex-snapshot
Validation
inferred← citeshttps://doi.org/10.1007/978-981-97-0700-3_19openalex-snapshot
A Novel Ensemble Approach for Colon Cancer Detection Over the Multiclass Colon Dataset
inferred← citeshttps://doi.org/10.1016/j.artmed.2026.103506openalex-snapshot
Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer
inferred← citeshttps://doi.org/10.1016/j.clcc.2025.09.003openalex-snapshot
Machine Learning-Based Prediction Model for Early-Onset Colorectal Cancer Risk: A Systematic Review and Meta-Analysis
inferred← citeshttps://doi.org/10.1016/j.ejca.2025.115440openalex-snapshot
Artificial intelligence to predict cancer risk, are we there yet? A comprehensive review across cancer types
inferred← citeshttps://doi.org/10.1016/j.ijmedinf.2025.106170openalex-snapshot
Machine learning in colorectal cancer prediction and diagnosis: a systematic review of models' performance
inferred← citeshttps://doi.org/10.1016/j.procs.2024.06.415openalex-snapshot
Artificial Intelligence in primary care: Intelligent risk predicting platform for non-communicable chronic diseases
inferred← citeshttps://doi.org/10.1016/j.tjnut.2024.03.002openalex-snapshot
A Perspective Review on Diet Quality, Excess Adiposity, and Chronic Psychosocial Stress and Implications for Early-Onset Colorectal Cancer
inferred← citeshttps://doi.org/10.1038/s41698-024-00719-2openalex-snapshot
Development and validation of machine learning models for young-onset colorectal cancer risk stratification
inferred← citeshttps://doi.org/10.1053/j.gastro.2023.05.019openalex-snapshot
Risk Classification After Colonoscopy and Polypectomy: Are We Always Fighting the Last War?
inferred← citeshttps://doi.org/10.1101/2024.07.17.24310573openalex-snapshot
Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data
inferred← citeshttps://doi.org/10.1109/cbms58004.2023.00219openalex-snapshot
Machine learning for rectal cancer prediction based on metabolic changes on amino acids
inferred← citeshttps://doi.org/10.1109/embc58623.2025.11252666openalex-snapshot
Enhancing Colon Cancer Risk Prediction in Machine Learning Models using Polygenic Risk Scores
inferred← citeshttps://doi.org/10.2196/64506openalex-snapshot
Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study
inferred← citeshttps://doi.org/10.3390/biomedicines12091955openalex-snapshot
Development and Validation of Machine Learning Algorithms for Prediction of Colorectal Polyps Based on Electronic Health Records
inferred← citeshttps://doi.org/10.3390/medicina62101875openalex-snapshot
Toward Precision Colorectal Cancer Screening: Integrating Clinical Risk Stratification, Molecular Biomarkers, and Artificial Intelligence
inferred← citeshttps://doi.org/10.52927/jdcr.2025.13.3.224openalex-snapshot
The Use of Artificial Intelligence in Colorectal Cancer Risk Prediction Models: A Review
inferred← citeshttps://doi.org/10.56238/homeiisevenhealth-128openalex-snapshot
Proactive approach in primary health care: a reference model for chronic disease risk prediction
inferred← citeshttps://doi.org/10.61940/jimt.230005openalex-snapshot
Prospective pilot trial for evaluating the feasibility of an artificial intelligence algorithm for predicting the risk of colorectal adenoma using health screening questionnaire
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.