ArticleClinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association2026
Natural Language Processing Algorithm Accurately Classifies Diverticulitis-Related Complications and Predicts Long-Term Outcomes.
Article in Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
BACKGROUND &
aimsDiagnostic codes lack the precision to identify specific complications of diverticulitis, limiting their utility in large-scale, real-world data. We developed a natural language processing (NLP) algorithm to classify diverticulitis and associated features using computed tomography (CT) reports.
methodsUsing data from Mass General Brigham Research Patient Data Registry (1979-2024), we identified patients with a diagnosis code for diverticular disease (International Classification of Diseases [ICD]-9: 562; ICD-10: K57) and a prior abdominopelvic CT report. We developed and validated our NLP algorithm to detect diverticulitis and associated features. We subsequently investigated the associations between NLP-defined severity at first diagnosis (ie, uncomplicated, mild, severe, or chronic complications) and risk of severe diverticulitis recurrence using a Cox proportional hazards regression model. We assessed the predictive value of NLP-detected features using random forest models.
resultsThe NLP algorithm achieved positive and negative predictive values of 82.8% to 99.9%, outperforming both ICD codes and a generalist large language model. Among 16,349 patients with NLP-detected diverticulitis, 3192 developed severe recurrence over 76,736 person-years. Compared with uncomplicated diverticulitis, the multivariable-adjusted hazard ratio (HR) for severe recurrence was 1.39 (95% confidence interval [CI], 1.14-1.69) for mild complications, 3.02 (95% CI, 2.80-3.27) for severe complications, and 5.41 (95% CI, 4.78-6.13) for chronic complications. NLP-detected features significantly improved the prediction of severe diverticulitis recurrence compared with codified variables.
conclusionsOur NLP algorithm accurately classifies diverticulitis features, facilitating the construction of large and high-quality electronic health record-based cohorts. Severity at initial diagnosis predicts risk of severe recurrence, supporting the use of artificial intelligence for risk stratification and long-term management.
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