Application of machine learning algorithms to recipient-related data for the prediction of short-term survival following liver transplantation
Bibliographic record
Abstract
Background and Objective: Current prediction of short-term survival after liver transplantation (LT) primarily relies on linear clinical scores such as the MELD, Donor-MELD, or Balance-of-Risk score. However, these model scores often provide limited predictive accuracy and depend on donor-related parameters that are only available shortly before transplantation, limiting their use for early risk stratification on the waiting list. The aim of this study was to develop and evaluate a recipient-based machine learning (ML) model to predict short-term post-transplant survival, using only variables available before organ allocation. Materials and Methods: Clinical data from 1260 LT recipients were used to train and validate models. Various algorithms were evaluated, including Random Forest, XGBoost, SVMs, and a Neural Network. Model discrimination was assessed using receiver operating characteristic (ROC) curves and various evaluation metrics. SHAP (Shapley Additive Explanations) was used to evaluate the relative importance of each variable based on its respective Shapley value. Results: The final Random Forest model, developed using a subset of clinically relevant parameters selected from the metadata and SHAP analysis, demonstrated excellent predictive performance for 1-year post-transplant survival, achieving an AUC of 0.88. Among the top predictors, hemoglobin emerged as a strong positive factor for survival, while elevated C-reactive protein was associated with a significantly reduced likelihood of predicted survival. Additional key variables included leukocyte count, international normalized ratio, and serum creatinine—each negatively associated with survival. Bilirubin (both direct and total) and serum sodium contributed moderately to the model’s prediction, while iron and albumin had minor yet still relevant impacts. In contrast, demographic and static recipient characteristics such as age, sex, and body size showed minimal individual predictive value. Conclusion: This study demonstrates that advanced machine learning approaches based solely on recipient data can improve the prediction of postoperative survival in LT recipients. These findings highlight the potential of data-driven models to support early risk stratification, patient prioritization, and clinical decision-making in LT programs. Publication History Article published online: 04 September 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".