Training and Performance of an Electrocardiogram-Enabled Machine Learning Model for Detection of Advanced Chronic Liver Disease
An ECG-enabled ML model affords great promise in identifying advanced CLD in low resource areas.
Authors
- Patrick S. Kamath (ORCID: https://orcid.org/0000-0002-7888-1165)
- Puru Rattan (ORCID: https://orcid.org/0000-0001-7410-488X)
- Paul A. Friedman (ORCID: https://orcid.org/0000-0001-5052-2948)
- Alina M. Allen (ORCID: https://orcid.org/0000-0002-8393-8410)
- Peter A. Noseworthy (ORCID: https://orcid.org/0000-0002-4308-0456)
- Zachi Itzhak Attia (ORCID: https://orcid.org/0000-0002-9706-7900)
- Vijay H. Shah (ORCID: https://orcid.org/0000-0001-7620-573X)
- Beatriz Sordi Chara (ORCID: https://orcid.org/0000-0001-6205-7596)
- Douglas A. Simonetto (ORCID: https://orcid.org/0000-0003-4095-8144)
- Joseph Ahn (ORCID: https://orcid.org/0000-0001-6994-2870)
- Aidan F. Mullan (ORCID: https://orcid.org/0000-0001-8076-6935)
- Kan Liu (ORCID: https://orcid.org/0000-0001-5625-8180)
Institutions
- Mayo Clinic (US)
- WinnMed (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- The American Journal of Gastroenterology
- Published
- 2025-03-27
- DOI
- https://doi.org/10.14309/ajg.0000000000003433
- Citations
- 2
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 2.71