Researchdeep learningliver transplantsurvival analysis
Deep Learning Forecasts Waitlist Outcomes in MASH
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Relevance Score
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).
