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Related Concept Videos

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Related Experiment Video

Updated: Jan 9, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Accurate and Scalable Classification of Colonoscopy Neoplasia Using Machine Learning and Natural Language Processing.

Brendan Broderick1, Jason Greenwood2, Douglas Mahoney3

  • 1Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, USA.

Clinical and Translational Gastroenterology
|December 9, 2025
PubMed
Summary
This summary is machine-generated.

A machine learning model accurately categorizes colorectal neoplasia from electronic health records, improving colonoscopy quality monitoring. This natural language processing system enhances the detection of adenomas and serrated lesions.

Keywords:
natural language processingneoplasia detection ratepathology report analysispredictive modelingrandom forest

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Area of Science:

  • Medical informatics
  • Machine learning in healthcare
  • Gastroenterology and oncology

Background:

  • Colorectal cancer (CRC) is a leading cause of cancer death in the US.
  • Colonoscopy is the primary screening method for CRC prevention.
  • Accurate identification of colorectal neoplasia from endoscopy and pathology is crucial for quality assessment.

Purpose of the Study:

  • To assess the feasibility of a random forest machine learning model for categorizing colorectal neoplasia.
  • To develop a natural language processing (NLP) system for classifying findings from electronic health records.

Main Methods:

  • A retrospective cohort study was conducted at a large academic institution.
  • A rule-based algorithm was developed to categorize neoplasia from endoscopy and pathology data.
  • A random forest NLP system was trained on unstructured pathology findings and validated on an independent set.

Main Results:

  • The model was trained on 35,953 pathology reports and 95,188 colonoscopy reports.
  • The NLP system achieved high accuracy on an independent validation set.
  • Area Under the Curve (AUC) values were 0.997 for adenoma, 0.99 for serrated, and 0.99 for advanced lesions.

Conclusions:

  • A random forest-based NLP system accurately and explainably classifies colonoscopy results.
  • NLP combined with machine learning offers a scalable strategy for colonoscopy quality monitoring.
  • This approach can improve the accuracy of neoplasia detection and patient outcomes.