ArticleScientific reports2020
Finding undiagnosed patients with hepatitis C infection: an application of artificial intelligence to patient claims data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 52 citations in OpenAlex.
- Artificial intelligence and personalised medicine in liver cancer.Journal of hepatology · 2026Review
- Review
- Artificial Intelligence in Population-Level Gastroenterology and Hepatology: A Comprehensive Review of Public Health Applications and Quantitative Impact.Digestive diseases and sciences · 2026Review
- Potential Cost-Effectiveness of Machine Learning-Enabled Primary Care Identification of Hepatitis C Virus Patients in the US.Viruses · 2026Article
- Article
- Uncloaking the black-box: the need for explainable artificial intelligence in clinical microbiology and infectious diseases applications.Frontiers in public health · 2026Review
- Development and Validation of a Machine Learning-Based Screening Algorithm to Predict High-Risk Hepatitis C Infection.Open forum infectious diseases · 2025Article
- Prioritising viral hepatitis elimination to prevent hepatocellular carcinoma: A public health approach for effective preventive hepatology.JHEP reports : innovation in hepatology · 2025Review
- Identifying and characterising asthma subgroups at high risk of severe exacerbations using machine learning and longitudinal real-world data.BMJ health & care informatics · 2025Article
- Transformative Insights into Community-Acquired Pressure Injuries Among the Elderly: A Big Data Analysis.Healthcare (Basel, Switzerland) · 2025Article
- Hepatitis C Virus-Pediatric and Adult Perspectives in the Current Decade.Pathogens (Basel, Switzerland) · 2024Review
- The role of artificial intelligence in the management of liver diseases.The Kaohsiung journal of medical sciences · 2024Review
- Ensemble for evaluating diagnostic efficacy of non-invasive indices in predicting liver fibrosis in untreated hepatitis C virus population.World journal of methodology · 2024Article
- Uncovering the burden of hidradenitis suppurativa misdiagnosis and underdiagnosis: a machine learning approach.Frontiers in medical technology · 2024Article
- Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking.Journal of personalized medicine · 2023Review
- Finding undiagnosed patients with hepatitis C virus: an application of machine learning to US ambulatory electronic medical records.BMJ health & care informatics · 2023Article
- Identifying individuals with undiagnosed post-traumatic stress disorder in a large United States civilian population - a machine learning approach.BMC psychiatry · 2022Article
- Predicting misdiagnosed adult-onset type 1 diabetes using machine learning.Diabetes research and clinical practice · 2022Article
- Burden of chronic hepatitis B and C infections in 2015 and future trends in Japan: A simulation study.The Lancet regional health. Western Pacific · 2022Article
- Machine learning using longitudinal prescription and medical claims for the detection of non-alcoholic steatohepatitis (NASH).BMJ health & care informatics · 2022Observational
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatitis C virus (HCV) remains a significant public health challenge with approximately half of the infected population untreated and undiagnosed. In this retrospective study, predictive models were developed to identify undiagnosed HCV patients using longitudinal medical claims linked to prescription data from approximately ten million patients in the United States (US) between 2010 and 2016. Features capturing information on demographics, risk factors, symptoms, treatments and procedures relevant to HCV were extracted from patients' medical history. Predictive algorithms were developed based on logistic regression, random forests, gradient boosted trees and a stacked ensemble. Descriptive analysis indicated that patients exhibited known symptoms of HCV on average 2-3 years prior to their diagnosis. The precision was at least 95% for all algorithms at low levels of recall (10%). For recall levels >50%, the stacked ensemble performed best with a precision of 97% compared with 87% for the gradient boosted trees and just 31% for the logistic regression. For context, the Center for Disease Control recommends screening in an at-risk sub-population with an estimated HCV prevalence of 2.23%. The artificial intelligence (AI) algorithm presented here has a precision which is substantially higher than the screening rates associated with recommended clinical guidelines, suggesting that AI algorithms have the potential to provide a step change in the effectiveness of HCV screening.
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Registered trials
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.