Deep Learning Forecasts Waitlist Outcomes in MASH
Researchers developed a deep-learning competing risk model (DeepHit) to forecast death and transplant trajectories for 17,551 patients with MASH cirrhosis listed for liver transplant using SRTR data and external validation at University Health Network. DeepHit achieved competing event coherence (CEC) scores of 0.813, 0.811, 0.794 and 0.772 at 1, 3, 6 and 12 months respectively; random survival forests had higher concordance overall while DeepHit improved transplant Brier score at 12 months (0.206 vs 0.228).
Scoring Rationale
Strong registry-based validation and clinical applicability, tempered by modest methodological novelty over existing survival models.
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Sources
- Read OriginalForecasting Waitlist Trajectories for Patients With Metabolic Dysfunction–Associated Steatohepatitis Cirrhosis: A Neural Network Competing Risk Analysisjmir.org
