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Updated: Mar 27, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Natural Language Processing Algorithm Accurately Classifies Diverticulitis-Related Complications and Predicts

Wenjie Ma1, Yilun Wu1, Prasanna K Challa1

  • 1Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.

Clinical Gastroenterology and Hepatology : the Official Clinical Practice Journal of the American Gastroenterological Association
|March 25, 2026
PubMed
Summary
This summary is machine-generated.

A new natural language processing (NLP) algorithm accurately classifies diverticulitis severity from CT reports. Initial severity predicts severe recurrence risk, improving patient management.

Keywords:
Artificial IntelligenceDiagnosticsDiverticulitisNatural Language Processing

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Area of Science:

  • Medical Informatics
  • Computational Pathology
  • Gastroenterology

Background:

  • Diagnostic codes lack precision for diverticulitis complications in real-world data.
  • Computed tomography (CT) reports contain detailed information on diverticulitis.
  • Natural language processing (NLP) can extract features from unstructured text.

Purpose of the Study:

  • Develop and validate an NLP algorithm to classify diverticulitis and associated features from CT reports.
  • Assess the association between NLP-defined initial diverticulitis severity and severe recurrence risk.
  • Evaluate the predictive value of NLP-detected features for severe diverticulitis recurrence.

Main Methods:

  • Utilized Mass General Brigham Research Patient Data Registry (1979-2024).
  • Developed and validated an NLP algorithm on abdominopelvic CT reports for patients with diverticular disease codes.
  • Employed Cox proportional hazards regression and random forest models to analyze recurrence risk and feature prediction.

Main Results:

  • The NLP algorithm demonstrated high positive (82.8%) and negative (99.9%) predictive values, outperforming ICD codes and a general large language model.
  • Among 16,349 patients, initial diverticulitis severity (mild, severe, chronic) significantly increased the hazard ratio for severe recurrence (1.39 to 5.41).
  • NLP-detected features significantly improved prediction of severe diverticulitis recurrence compared to codified variables.

Conclusions:

  • An NLP algorithm accurately classifies diverticulitis features from CT reports, enabling large, high-quality electronic health record (EHR) cohorts.
  • Initial diverticulitis severity is a strong predictor of severe recurrence risk.
  • AI-driven risk stratification using NLP holds promise for long-term diverticulitis management.