ArticleDigestive diseases and sciences2026
Identifying Patients at High Risk for Colorectal Carcinoma Using the Electronic Health Record.
Article in Digestive diseases and sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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.
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Who cites it
2 citing papers in PubMed.
- Bridging the Precancerous Gap in Colorectal Cancer Through AI-Enhanced Liquid Biopsy, Endoscopy, and Digital Pathology.Cancers · 2026Review
- When High Accuracy Meets Conditioned Outcome-Interpreting EHR-Based Risk Models for Colorectal Neoplasia.Digestive diseases and sciences · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundColorectal cancer (CRC) is the fourth most common and second deadliest cancer in the US. Screening is effective at reducing CRC incidence and mortality, but rates of screening remain suboptimal. There are no sensitive machine learning models for accurately identifying individuals at risk for colorectal cancer or precancerous polyps.
objectivesThe aim of our study was to develop and validate a novel machine learning model that uses multimodal Electronic Health Record (EHR) data, including the most recent complete blood count (CBC), basic metabolic panel (BMP), ICD codes, and medications, to estimate a patient's likelihood of having CRC or an advanced precancerous lesion.
methodsWe developed ColAI, an L1-regularized logistic regression model trained on 1-year trailing EHR data, to predict CRC or advanced adenoma at screening colonoscopy. Labs are treated as continuous variables, while ICD codes and medications are represented as binary indicators of presence. ColAI was trained using 87,825 screening colonoscopies and validated using 21,957 independent colonoscopies between August 1, 2020, and March 31, 2024, from the NYU Langone Health system.
resultsColAI achieved an AUROC of 0.93 for CRC and 0.98 for CRC or advanced adenoma. Performance remained consistent across different hospitals and time periods within NYU Langone, demonstrating strong generalizability. Performance also remained consistent between first and follow-up colonoscopies, decreasing concern for selection bias.
conclusionsColAI accurately identifies patients at elevated risk for CRC using only routine EHR data. It has the potential to enhance targeted outreach to high-risk, unscreened individuals and improve early cancer detection at the population level.
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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.