Development and Validation of a Machine Learning–Based Screening Algorithm to Predict High-Risk Hepatitis C Infection - Takeaways - MDSpire

Development and Validation of a Machine Learning–Based Screening Algorithm to Predict High-Risk Hepatitis C Infection

  • By

  • Suk-Chan Jang

  • Wei-Hsuan Lo-Ciganic

  • Pilar Hernandez-Con

  • Chanakan Jenjai

  • James Huang

  • Ashley Stultz

  • Shunhua Yan

  • Debbie L Wilson

  • Ashley Norse

  • Faheem W Guirgis

  • Robert L Cook

  • Christine Gage

  • Khoa A Nguyen

  • Patrick Hornes

  • Yonghui Wu

  • David R Nelson

  • Haesuk Park

  • August 15, 2025

  • 0 min

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  • 1

    HCV infections are rising in the U.S., particularly amid the opioid epidemic, with many individuals unaware due to asymptomatic cases.

  • 2

    A machine learning algorithm was developed to identify individuals at high risk for HCV infection using data from the OneFlorida+ database.

  • 3

    The gradient boosting machine model outperformed other models, achieving a C statistic of 0.916 and identifying 1 positive case per 6 tests.

  • 4

    Among those testing positive for HCV, 75.63% were captured in the highest risk decile, demonstrating effective risk stratification.

  • 5

    The study highlights the potential of machine learning to enhance targeted HCV screening in clinical settings, addressing practical challenges.

Original Source(s)

Development and Validation of a Machine Learning–Based Screening Algorithm to Predict High-Risk Hepatitis C Infection

  • by Suk-Chan Jang, Wei-Hsuan Lo-Ciganic, Pilar Hernandez-Con, Chanakan Jenjai, James Huang, Ashley Stultz, Shunhua Yan, Debbie L Wilson, Ashley Norse, Faheem W Guirgis, Robert L Cook, Christine Gage, Khoa A Nguyen, Patrick Hornes, Yonghui Wu, David R Nelson, Haesuk Park

  • August 15, 2025

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