Risk Prediction in Patients With Metabolic Dysfunction–Associated Steatohepatitis Using Natural Language Processing
We described a cohort of real-world MASH patients using a new NLP algorithm and found several potential predictors of progression to all-cause mortality, cirrhosis, and liver transplantation. The use of NLP to characterize these patients can help support the development of future interventional trials in MASH.
Authors
- Jordan Guillot (ORCID: https://orcid.org/0000-0002-9091-0571)
- Balu Bhasuran (ORCID: https://orcid.org/0000-0002-9890-4627)
- Christopher Y. K. Williams (ORCID: https://orcid.org/0000-0001-8867-1623)
- R. Ravi Shankar (ORCID: https://orcid.org/0000-0002-3709-0965)
- Boshu Ru (ORCID: https://orcid.org/0000-0001-9620-1306)
- Vivek A. Rudrapatna (ORCID: https://orcid.org/0000-0003-1789-3004)
- Jin Ge (ORCID: https://orcid.org/0000-0002-9204-4718)
- Gail Fernandes (ORCID: https://orcid.org/0000-0002-9654-2634)
- Xiao Zhang (ORCID: https://orcid.org/0000-0002-7584-0712)
- Joe Yang
- Shadera Azzam
Institutions
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
- University of California, San Francisco (US)
Publication Details
- Journal
- Gastro Hep Advances
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.gastha.2025.100701
- Citations
- 3
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 4.06