ArticleHepatology (Baltimore, Md.)2025
Evaluating the positive predictive value of code-based identification of cirrhosis and its complications utilizing GPT-4.
Article in Hepatology (Baltimore, Md.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Identification of Primary Sclerosing Cholangitis: ICD-10 Code Validation and Comparison with a Large Language Model Approach.Digestive diseases and sciences · 2026Article
- Patients who underwent TIPS face increases in hernia incarceration rates compared to patients managed with paracentesis: An analysis of prevalence and risk factors.Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society · 2026Article
- Large Language Models for Diagnosis and Prognosis of Chronic Liver Diseases: A Systematic Review.Health science reports · 2026Review
- Identification of primary sclerosing cholangitis: ICD-10 code validation and comparison with a large language model approach.Research square · 2026Article
- Large Language Models can Identify the Presence of MASH and Extract VCTE Measurements from Unstructured Documentation.Digestive diseases and sciences · 2026Article
- Application of artificial intelligence in hepatology.Frontiers in digital health · 2026Review
- Development and Validation of a Large Language Model Case Identification Strategy for Eosinophilic Esophagitis.Gastro hep advances · 2026Article
- Large language models in hepatology: A systematic review.Hepatology forum · 2026Review
- Evaluating large language models' performance in answering common questions on drug-induced liver injury.JHEP reports : innovation in hepatology · 2025Article
- Big Data Analytics in Large Cohorts: Opportunities and Challenges for Research in Hepatology.Seminars in liver disease · 2025Review
- Evaluating the Effectiveness of Large Language Models in Providing Patient Education for Chinese Patients With Ocular Myasthenia Gravis: Mixed Methods Study.Journal of medical Internet research · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
BACKGROUND AND
aimsDiagnosis code classification is a common method for cohort identification in cirrhosis research, but it is often inaccurate and augmented by labor-intensive chart review. Natural language processing using large language models (LLMs) is a potentially more accurate method. To assess LLMs' potential for cirrhosis cohort identification, we compared code-based versus LLM-based classification with chart review as a "gold standard." APPROACH AND
resultsWe extracted and conducted a limited chart review of 3788 discharge summaries of cirrhosis admissions. We engineered zero-shot prompts using a Generative Pre-trained Transformer 4 to determine whether cirrhosis and its complications were active hospitalization problems. We calculated positive predictive values (PPVs) of LLM-based classification versus limited chart review and PPVs of code-based versus LLM-based classification as a "silver standard" in all 3788 summaries. Compared to gold standard chart review, code-based classification achieved PPVs of 82.2% for identifying cirrhosis, 41.7% for HE, 72.8% for ascites, 59.8% for gastrointestinal bleeding, and 48.8% for spontaneous bacterial peritonitis. Compared to the chart review, Generative Pre-trained Transformer 4 achieved 87.8%-98.8% accuracies for identifying cirrhosis and its complications. Using LLM as a silver standard, code-based classification achieved PPVs of 79.8% for identifying cirrhosis, 53.9% for HE, 55.3% for ascites, 67.6% for gastrointestinal bleeding, and 65.5% for spontaneous bacterial peritonitis.
conclusionsLLM-based classification was highly accurate versus manual chart review in identifying cirrhosis and its complications. This allowed us to assess the performance of code-based classification at scale using LLMs as a silver standard. These results suggest LLMs could augment or replace code-based cohort classification and raise questions regarding the necessity of chart review.
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