ArticleJournal of the American Medical Informatics Association : JAMIA2021
Development of a novel machine learning model to predict presence of nonalcoholic steatohepatitis.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- AI-Based Models for Risk Prediction in MASLD: A Systematic Review.Digestive diseases and sciences · 2026Pooled it
- Multivariate time-series forecasting of liver biomarkers from longitudinal lifestyle data for nonalcoholic steatohepatitis detection.JAMIA open · 2026Article
- Harnessing artificial intelligence for the assessment of liver fibrosis and steatosisWorld journal of gastroenterology · 2026Review
- Predicting metabolic dysfunction-associated steatotic liver disease risk using patient-derived induced pluripotent stem cells.Stem cells translational medicine · 2026Article
- Development of a machine learning model to identify individuals with VCTE-derived at-risk MASH in a Spanish Mediterranean region.Frontiers in digital health · 2026Article
- Predicting Metabolic Dysfunction-Associated Fatty Liver Disease Phenotypes Among Adults: 2-Stage Contrastive Learning Method.JMIR medical informatics · 2025Article
- Big data dimensionality reduction-based supervised machine learning algorithms for NASH diagnosis.BMC bioinformatics · 2025Article
- AI-Based Platelet-Independent Noninvasive Test for Liver Fibrosis in MASLD Patients.JGH open : an open access journal of gastroenterology and hepatology · 2025Article
- Innovative approaches to metabolic dysfunction-associated steatohepatitis diagnosis and stratification.Non-coding RNA research · 2025Article
- Extracellular vesicle-mediated approaches for the diagnosis and therapy of MASLD: current advances and future prospective.Lipids in health and disease · 2025Review
- An explainable machine learning model for prediction of high-risk nonalcoholic steatohepatitis.Scientific reports · 2024Article
- Identification of signature gene set as highly accurate determination of metabolic dysfunction-associated steatotic liver disease progression.Clinical and molecular hepatology · 2024Article
- Artificial Intelligence in Liver Diseases: Recent Advances.Advances in therapy · 2024Review
- Identification of Fast Progressors Among Patients With Nonalcoholic Steatohepatitis Using Machine Learning.Gastro hep advances · 2024Article
- Development and application of a machine learning-based predictive model for obstructive sleep apnea screening.Frontiers in big data · 2024Article
- Artificial Intelligence in Identifying Patients With Undiagnosed Nonalcoholic Steatohepatitis.Journal of health economics and outcomes research · 2024Article
- A hierarchical multilabel graph attention network method to predict the deterioration paths of chronic hepatitis B patients.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- NASHmap: clinical utility of a machine learning model to identify patients at risk of NASH in real-world settings.Scientific reports · 2023Article
- Article
- Non-invasive biomarkers for liver inflammation in non-alcoholic fatty liver disease: present and future.Clinical and molecular hepatology · 2023Review
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop a computer model to predict patients with nonalcoholic steatohepatitis (NASH) using machine learning (ML). MATERIALS AND
methodsThis retrospective study utilized two databases: a) the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) nonalcoholic fatty liver disease (NAFLD) adult database (2004-2009), and b) the Optum® de-identified Electronic Health Record dataset (2007-2018), a real-world dataset representative of common electronic health records in the United States. We developed an ML model to predict NASH, using confirmed NASH and non-NASH based on liver histology results in the NIDDK dataset to train the model.
resultsModels were trained and tested on NIDDK NAFLD data (704 patients) and the best-performing models evaluated on Optum data (~3,000,000 patients). An eXtreme Gradient Boosting model (XGBoost) consisting of 14 features exhibited high performance as measured by area under the curve (0.82), sensitivity (81%), and precision (81%) in predicting NASH. Slightly reduced performance was observed with an abbreviated feature set of 5 variables (0.79, 80%, 80%, respectively). The full model demonstrated good performance (AUC 0.76) to predict NASH in Optum data. DISCUSSION: The proposed model, named NASHmap, is the first ML model developed with confirmed NASH and non-NASH cases as determined through liver biopsy and validated on a large, real-world patient dataset. Both the 14 and 5-feature versions exhibit high performance.
conclusionThe NASHmap model is a convenient and high performing tool that could be used to identify patients likely to have NASH in clinical settings, allowing better patient management and optimal allocation of clinical resources.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.