ArticleAmerican journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons2023
Predicting post-liver transplant outcomes in patients with acute-on-chronic liver failure using Expert-Augmented Machine Learning.
Article in American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Predicting 90-day mortality in patients with HBV-ACLF: A comparison of machine learning models and logistic regression.Medicine · 2026Article
- Beyond the Tipping Point: Advances in the Diagnosis and Management of Acute-on-Chronic Liver Failure and End-Stage Liver Disease.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance.Journal of clinical medicine · 2026Review
- Application of artificial intelligence in hepatology.Frontiers in digital health · 2026Review
- Development and validation of a novel prognostic model to predict 1-year post-transplant mortality for acute-on-chronic hepatitis B liver failure: a nationwide, multicentre, cohort study.EClinicalMedicine · 2025Article
- Generalizability of kidney transplant data in electronic health records - The Epic Cosmos database vs the Scientific Registry of Transplant Recipients.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2025Article
- Lesson learnt from 60 years of liver transplantation: Advancements, challenges, and future directions.World journal of transplantation · 2025Review
- Predicting offer burden to optimize batch sizes in simultaneously expiring kidney offers.Frontiers in artificial intelligence · 2025Article
- Predicting the gastrointestinal bleeding of HBV-related acute-on-chronic liver failure based on machine learning.Frontiers in medicine · 2025Article
- From prognostication to therapeutics: Four key questions to accelerate the development of therapeutics for patients with acute-on-chronic liver failure.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2024Article
- Common definitions and variables are needed for the United States to join the conversation on acute-on-chronic liver failure.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Liver transplantation (LT) is a treatment for acute-on-chronic liver failure (ACLF), but high post-LT mortality has been reported. Existing post-LT models in ACLF have been limited. We developed an Expert-Augmented Machine Learning (EAML) model to predict post-LT outcomes. We identified ACLF patients who underwent LT in the University of California Health Data Warehouse. We applied the RuleFit machine learning (ML) algorithm to extract rules from decision trees and create intermediate models. We asked human experts to rate the rules generated by RuleFit and incorporated these ratings to generate final EAML models. We identified 1384 ACLF patients. For death at 1 year, areas under the receiver-operating characteristic curve were 0.707 (confidence interval [CI] 0.625-0.793) for EAML and 0.719 (CI 0.640-0.800) for RuleFit. For death at 90 days, areas under the receiver-operating characteristic curve were 0.678 (CI 0.581-0.776) for EAML and 0.707 (CI 0.615-0.800) for RuleFit. In pairwise comparisons, both EAML and RuleFit models outperformed cross-sectional models. Significant discrepancies between experts and ML occurred in rankings of biomarkers used in clinical practice. EAML may serve as a method for ML-guided hypothesis generation in further ACLF research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.