OphthaDT: Generative Digital Twins for Forecasting Visual Acuity Trajectories in Ophthalmology

Precision medicine in ophthalmology requires accurate longitudinal predictions, but the fragmented nature of multimodal clinical data remains a barrier to forecasting. We introduce OphthaDT, an LLM-based digital twin for ophthalmology that serializes longitudinal patient histories from 3,220 patients across four Phase III clinical trials into structured narratives to forecast best corrected visual acuity (BCVA). In benchmarks spanning up to 100 weeks, OphthaDT demonstrated the lowest prediction error in neovascular age-related macular degeneration (nAMD), achieving an average mean absolute error (MAE) reduction of 6.0% compared to all baselines. In diabetic macular edema (DME), OphthaDT demonstrated competitive performance against all baselines while outperforming Random Forest and XGBoost by an average MAE reduction of 2.6% and 6.9%, respectively. Results reveal that OphthaDT's predictive advantage scales with trajectory complexity: whereas linear models remain effective for the more stable treatment responses of DME, OphthaDT's capacity is better suited for capturing the high longitudinal variability of nAMD. Finally, OphthaDT handles irregular sampling without imputation, positioning LLM-based clinical trajectory modeling as a methodology that could reduce patient burden and accelerate drug development.

TwinWeaver: An LLM-Based Foundation Model Framework for Pan-Cancer Digital Twins

Precision oncology requires forecasting clinical events and trajectories, yet modeling sparse, multi-modal clinical time series remains a critical challenge. We introduce TwinWeaver, an open-source framework that serializes longitudinal patient histories into text, enabling unified event prediction as well as forecasting with large language models, and use it to build Genie Digital Twin (GDT) on 93,054 patients across 20 cancer types. In benchmarks, GDT significantly reduces forecasting error, achieving a median Mean Absolute Scaled Error (MASE) of 0.87 compared to 0.97 for the strongest time-series baseline (p<0.001). Furthermore, GDT improves risk stratification, achieving an average concordance index (C-index) of 0.703 across survival, progression, and therapy switching tasks, surpassing the best baseline of 0.662. GDT also generalizes to out-of-distribution clinical trials, matching trained baselines at zero-shot and surpassing them with fine-tuning, achieving a median MASE of 0.75-0.88 and outperforming the strongest baseline in event prediction with an average C-index of 0.672 versus 0.648. Finally, TwinWeaver enables an interpretable clinical reasoning extension, providing a scalable and transparent foundation for longitudinal clinical modeling.

Graph neural network surrogate for strategic transport planning

As the complexities of urban environments continue to grow, the modelling of transportation systems become increasingly challenging. This paper explores the application of advanced Graph Neural Network (GNN) architectures as surrogate models for strategic transport planning. Building upon a prior work that laid the foundation with graph convolution networks (GCN), our study delves into the comparative analysis of established GCN with the more expressive Graph Attention Network (GAT). Additionally, we propose a novel GAT variant (namely GATv3) to address over-smoothing issues in graph-based models. Our investigation also includes the exploration of a hybrid model combining both GCN and GAT architectures, aiming to investigate the performance of the mixture. The three models are applied to various experiments to understand their limits. We analyse hierarchical regression setups, combining classification and regression tasks, and introduce fine-grained classification with a proposal of a method to convert outputs to precise values. Results reveal the superior performance of the new GAT in classification tasks. To the best of the authors' knowledge, this is the first GAT model in literature to achieve larger depths. Surprisingly, the fine-grained classification task demonstrates the GCN's unexpected dominance with additional training data. This shows that synthetic data generators can increase the training data, without overfitting issues whilst improving model performance. In conclusion, this research advances GNN based surrogate modelling, providing insights for refining GNN architectures. The findings open avenues for investigating the potential of the newly proposed GAT architecture and the modelling setups for other transportation problems.