TransplantPredictor: A deep learning framework for liver transplant matching and survival prediction

Liver transplant outcome prediction presents a complex multi-modal data fusion engineering challenge involving heterogeneous clinical data, temporal dependencies, and interpretability constraints. This paper presents TransplantPredictor, a novel ensemble deep learning framework that systematically addresses these challenges through innovative architectural design and algorithmic optimization. Unlike previous single-model approaches in transplant prediction literature, our framework systematically integrates multiple complementary deep learning architectures with progressive transfer learning to overcome the fundamental limitations of single-paradigm methodologies. We propose a three-component ensemble architecture integrating LSTM networks for temporal sequence modeling, RNN for clinical progression patterns, and Gradient Boosting for non-linear tabular relationships. The ensemble employs advanced stacking with engineered meta-features and implements progressive transfer learning to overcome limited medical data constraints. The framework incorporates unified explainability using SHAP and LIME with consistency validation, while systematic preprocessing handles missing data through MICE and class imbalance via SMOTE. Extensive evaluation on 250 liver transplantation cases demonstrates superior performance of the proposed framework. The final TransplantPredictor with transfer learning achieves AUC-ROC of 0.941, F1-score of 87.9%, and accuracy of 91.2%, significantly outperforming the traditional MELD scoring system (84.9% accuracy) by 6.3%. Individual component analysis shows the ensemble approach provides substantial improvements over single models: LSTM (84.2%), RNN (82.5%), and Gradient Boosting (85.1%). The progressive transfer learning strategy contributes an additional 5.2% F1-score improvement compared to existing models. Ablation studies confirm the effectiveness of each component and the benefits of combining multiple models with transfer learning. The framework provides a practical solution for medical AI applications requiring accurate and interpretable predictions. • TransplantPredictor integrates LSTM, RNN, and Gradient Boosting models through sophisticated stacking and voting mechanisms, achieving superior predictive performance (91.2% accuracy, 0.941 AUC-ROC) compared to traditional MELD scoring systems (84.9% accuracy). • Progressive transfer learning on 200,000+ UNOS cases followed by fine-tuning on institutional data overcomes limited medical dataset constraints, providing 5.2% F1-score improvement over models trained from scratch. • SHAP and LIME techniques provide both global feature importance and local case-specific explanations, ensuring clinical interpretability and identifying key factors beyond traditional MELD scoring that influence transplant outcomes. • The framework systematically handles heterogeneous clinical data including temporal laboratory sequences, categorical variables, and tabular donor-recipient parameters through specialized preprocessing and feature engineering pipelines. • Demonstrates 6.3% accuracy improvement over MELD-based approaches with potential to reduce waitlist mortality by 5.7% and improve successful transplantation rates by 8.2%, providing actionable insights for optimized organ allocation and post-transplant care planning.