ArticleDigestive diseases and sciences2017
Accurate Identification of Fatty Liver Disease in Data Warehouse Utilizing Natural Language Processing.
Article in Digestive diseases and sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 4 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- A foundation systematic review of natural language processing applied to gastroenterology & hepatology.BMC gastroenterology · 2025Pooled it
- Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review.Reviews in endocrine & metabolic disorders · 2022Pooled it
- Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications.Applied clinical informatics · 2022Pooled it
- A systematic review of natural language processing applied to radiology reports.BMC medical informatics and decision making · 2021Pooled it
- Natural language processing and LLMs in liver imaging: a practical review of clinical applications.Abdominal radiology (New York) · 2026Review
- Longitudinal serum total bile acid trajectories and risk of metabolic dysfunction-associated fatty liver disease: a retrospective cohort study.European journal of medical research · 2025Article
- Liver biopsy in the modern era: from traditional techniques to artificial intelligence and multi-omics integration.Frontiers in medicine · 2025Review
- Artificial intelligence in imaging for liver disease diagnosis.Frontiers in medicine · 2025Review
- A natural language processing algorithm accurately classifies steatotic liver disease pathology to estimate the risk of cirrhosis.Hepatology communications · 2024Article
- Artificial Intelligence in Liver Diseases: Recent Advances.Advances in therapy · 2024Review
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Large-scale identification of undiagnosed hepatic steatosis using natural language processing.EClinicalMedicine · 2023Article
- Clinical Course and Outcomes of Patients with Nonalcoholic Fatty Liver Disease-Related Hepatocellular Cancer (NAFLD-HCC).Digestive diseases and sciences · 2023Article
- Longitudinal changes in fibrosis markers are associated with risk of cirrhosis and hepatocellular carcinoma in non-alcoholic fatty liver disease.Journal of hepatology · 2023Article
- "Beyond MELD" - Emerging strategies and technologies for improving mortality prediction, organ allocation and outcomes in liver transplantation.Journal of hepatology · 2022Review
- Transfer language space with similar domain adaptation: a case study with hepatocellular carcinoma.Journal of biomedical semantics · 2022Article
- Using a classification model for determining the value of liver radiological reports of patients with colorectal cancer.Frontiers in oncology · 2022Article
- Artificial intelligence in gastroenterology: A state-of-the-art review.World journal of gastroenterology · 2021Review
- Natural Language Processing for Surveillance of Cervical and Anal Cancer and Precancer: Algorithm Development and Split-Validation Study.JMIR medical informatics · 2020Article
- Risk of Cirrhosis and Hepatocellular Cancer in Patients With NAFLD and Normal Liver Enzymes.Hepatology (Baltimore, Md.) · 2020Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionNatural language processing is a powerful technique of machine learning capable of maximizing data extraction from complex electronic medical records.
methodsWe utilized this technique to develop algorithms capable of "reading" full-text radiology reports to accurately identify the presence of fatty liver disease. Abdominal ultrasound, computerized tomography, and magnetic resonance imaging reports were retrieved from the Veterans Affairs Corporate Data Warehouse from a random national sample of 652 patients. Radiographic fatty liver disease was determined by manual review by two physicians and verified with an expert radiologist. A split validation method was utilized for algorithm development.
resultsFor all three imaging modalities, the algorithms could identify fatty liver disease with >90% recall and precision, with F-measures >90%. DISCUSSION: These algorithms could be used to rapidly screen patient records to establish a large cohort to facilitate epidemiological and clinical studies and examine the clinic course and outcomes of patients with radiographic hepatic steatosis.
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