Federated learning (FL) enables privacy-preserving model training across distributed Electronic Health Records (EHRs), but its deployment remains limited by data-view heterogeneity, where institutions maintain incompatible local schemas. Most existing methods address this by enforcing flat, aligned data views, which require extensive cross-site preprocessing and manual harmonisation that often discards client-specific features, or by projecting inputs into a shared latent space, which sacrifices interpretability. We propose a modelling shift from conventional FL with vectorised inputs to a symbolic, relation-centric framework, where each client organises its EHR data as a structured, type-aware relational graph. This enables client-specific inference without requiring schema alignment and supports FL across heterogeneous data views. To model over these symbolic structures, we introduce an architecture that combines relation-aware message passing with a learnable feature relevance mechanism, jointly enabling accurate local predictions and client-specific interpretability while supporting parameter sharing across clients. Beyond strong performance on three real-world EHR datasets exhibiting data-view heterogeneity, we further show that our framework supports multimodal FL under modality-level heterogeneity. Using MC-MED, a publicly available multimodal emergency department dataset, we demonstrate that our method accommodates clients with partially missing modalities, highlighting its robustness and scalability in real-world clinical settings.

Federated Learning (FL) enables collaborative clinical modelling across distributed electronic health records (EHRs) without sharing sensitive patient data. However, variations in medical practice, documentation standards, and data collection across institutions create data-view heterogeneity, where clients possess different or only partially overlapping clinical feature sets. This misalignment hinders the use of standard FL methods. Existing approaches rely on complex preprocessing and manual harmonisation, which can cause information loss, reduce data utility, limit scalability, and restrict client-specific personalisation. To address these limitations, we propose Personalised Attention-based Federated Graph Network (PAFNet), a scalable FL framework that enables meaningful parameter exchange across heterogeneous clients by mapping their distinct data-views into a shared latent space through client-specific projection layers. It then applies a personalised adaptation mechanism using trainable parameter masks, allowing each client to selectively incorporate global model parameters relevant to its own feature set. This design preserves local specificity, improves generalisation, and removes the need for heavy manual preprocessing common in existing approaches. Across CURIAL, eICU, and MIMIC-III datasets, PAFNet consistently outperformed state-of-the-art data-view heterogeneity FL baselines, demonstrating strong generalisation under substantial differences in client feature sets. By enabling effective personalisation and cross-institutional knowledge sharing without extensive harmonisation, PAFNet offers a robust and scalable solution for the federated training of clinical models in data-view heterogeneous environments.

This paper presents Adaptive Parameter Optimisation (APO), a novel framework for optimising shared models across multiple clinical tasks, addressing the challenges of balancing strict parameter sharing—often leading to task conflicts—and soft parameter sharing, which may limit effective cross-task information exchange. The proposed APO framework leverages insights from the lazy behaviour observed in over-parameterised neural networks, where only a small subset of parameters undergo any substantial updates during training. APO dynamically identifies and updates task-specific parameters while treating parameters associated with other tasks as protected, limiting their modification to prevent interference. The remaining unassigned parameters remain unchanged, embodying the lazy training phenomenon. This dynamic management of task-specific, protected, and unclaimed parameters across tasks enables effective information sharing, preserves task-specific adaptability, and mitigates gradient conflicts without enforcing a uniform representation. Experimental results across diverse healthcare datasets demonstrate that APO surpasses traditional information-sharing approaches, such as multi-task learning and model-agnostic meta-learning, in improving task performance.

Federated Learning (FL) involves a server aggregating local models from clients to compute a global model. However, this process can struggle to position the global model in low-loss regions of the parameter space for all clients, resulting in subpar convergence and inequitable performance across clients. This issue is particularly pronounced in non-IID settings, common in clinical contexts, where variations in data distribution, class imbalance, and training sample sizes result in client heterogeneity. To address this issue, we propose a mode connectivity-based FL framework that ensures the global model resides within the overlapping low-loss regions of all clients in the parameter space. This framework models the low-loss regions as non-linear mode connections between the current global and local models, and optimises to identify an intersection among these mode connections to define the new global model. This approach enhances training stability and convergence, yielding better and more equitable performance compared to standard FL frameworks like federated averaging. Empirical evaluations across multiple healthcare datasets demonstrate the benefits of the proposed framework.

While machine learning algorithms hold promise for personalised medicine, their clinical adoption remains limited, partly due to biases that can compromise the reliability of predictions. In this article, we focus on sample selection bias (SSB), a specific type of bias where the study population is less representative of the target population, leading to biased and potentially harmful decisions. Despite being well-known in the literature, SSB remains scarcely studied in machine learning for healthcare. Moreover, the existing machine learning techniques try to correct the bias mostly by balancing distributions between the study and the target populations, which may result in a loss of predictive performance. To address these problems, our study illustrates the potential risks associated with SSB by examining SSB’s impact on the performance of machine learning algorithms. Most importantly, we propose a new research direction for addressing SSB, based on the target population identification rather than the bias correction. Specifically, we propose two independent networks (T-Net) and a multitasking network (MT-Net) for addressing SSB, where one network/task identifies the target subpopulation which is representative of the study population and the second makes predictions for the identified subpopulation. Our empirical results with synthetic and semi-synthetic datasets highlight that SSB can lead to a large drop in the performance of an algorithm for the target population as compared with the study population, as well as a substantial difference in the performance for the target subpopulations that are representative of the selected and the non-selected patients from the study population. Furthermore, our proposed techniques demonstrate robustness across various settings, including different dataset sizes, event rates and selection rates, outperforming the existing bias correction techniques.

Given radiology images, automatic radiology report generation aims to produce informative text that reports diseases. It can benefit current clinical practice in diagnostic radiology. Existing methods typically rely on large-scale medical datasets annotated by clinicians to train desirable models. However, for novel diseases, sufficient training data are typically not available. We propose a prompt-based deep learning framework, i.e., PromptLLM, to align, autoencode, and prompt the (large) language model to generate reports for novel diseases accurately and efficiently. Our method includes three major steps: 1) aligning visual images and textual reports to learn general knowledge across modalities from diseases where labeled data are sufficient, 2) autoencoding the LLM using unlabeled data of novel diseases to learn the specific knowledge and writing styles of the novel disease, and 3) prompting the LLM with learned knowledge and writing styles to report the novel diseases contained in the radiology images. Through the above three steps, with limited labels on novel diseases, we show that PromptLLM can rapidly learn the corresponding knowledge for accurate novel disease reporting. The experiments on COVID-19 and diverse thorax diseases show that our approach, utilizing 1% of the training data, achieves desirable performance compared to previous methods. It shows that our approach allows us to relax the reliance on labeled data that is common to existing methods. It could have a real-world impact on data analysis during the early stages of novel diseases.