The proliferation of healthcare data sources, including diverse imaging modalities and biochemical measurements, has created unprecedented opportunities for comprehensive disease prediction. Multi-modal clinical data, encompassing medical imaging reports, biochemical assays, and longitudinal clinical records, provides a rich foundation for developing sophisticated diagnostic models. Graph Neural Networks (GNNs) have emerged as a leading methodological framework, distinguished by their capacity to model complex inter-patient relationships and capture community structures within patient data. Despite their promise, current GNN-based approaches exhibit limitations in handling noisy, low-quality data and often impose overly restrictive graph smoothness constraints. These limitations can obscure patient-specific variations and compromise model robustness. To overcome these challenges, we propose HierSSL (Hierarchical Self-Supervised Learning), a novel multi-modal disease prediction framework that enhances representational learning through dual-scale self-supervision mechanisms operating at both local and global levels. HierSSL's architecture specifically addresses two critical aspects: 1) the capture of local inter-modality dependencies and global community patterns, and 2) the optimization of multi-modal feature integration through an innovative combination of feature consistency constraints and graph contrastive learning. Empirical evaluation across two distinct disease prediction datasets demonstrates that HierSSL achieves statistically significant performance improvements compared to state-of-the-art methods, highlighting its efficacy in robust multi-modal data integration for disease prediction tasks.

Food recommendation systems play a pivotal role in shaping dietary salubrity and fostering sustainable lifestyles by recommending recipes and foodstuffs that align with user preferences. Metadata information of a recipe, encompassing multi-modal descriptions, constituent ingredients, and health-related attributes, can furnish a more holistic perspective on the recipe's profile, thereby augmenting recommendation performance. However, existing state-of-the-art methods often overlook the inherent interdependencies between modalities, ingredients, and health factors, leaving the health information pertaining to recipe characteristics underexploited. Notably, our preliminary investigation on two datasets unveiled that the semantic divergence between health-related knowledge and collaborative filtering signals is more pronounced in comparison to other metadata information, thereby potentially impeding the efficacy of food recommendation systems. To address these limitations, we propose HealthRec, a novel multi-modal food recommendation framework with health-aware knowledge distillation. HealthRec employs a global graph representation learning module to capture high-order dependencies across diverse food-related relations, enriching the representations. Subsequently, a co-attention network is leveraged to capture local, recipe-level knowledge transfer between modality-related and ingredient-related embeddings. Additionally, we exploit external supervision signals derived from WHO recommendations, utilizing knowledge distillation during the training phase to transfer local health-aware knowledge into global collaborative embeddings. Extensive experimentation on real-world datasets demonstrates HealthRec's superiority compared to current state-of-the-art recommendation baselines, highlighting its effectiveness in modeling health-aware food recommendations.

Medicine package recommendation aims to assist doctors in clinical decision-making by recommending appropriate packages of medicines for patients. Current methods model this task as a multi-label classification or sequence generation problem, focusing on learning relationships between individual medicines and other medical entities. However, these approaches uniformly overlook the interactions between medicine packages and other medical entities, potentially resulting in a lack of completeness in recommended medicine packages. Furthermore, medicine commonsense knowledge considered by current methods is notably limited, making it challenging to delve into the decision-making processes of doctors. To solve these problems, we propose DIAGNN, a Dual-level Interaction Aware heterogeneous Graph Neural Network for medicine package recommendation. Specifically, DIAGNN explicitly models interactions of medical entities within electronic health records(EHRs) at two levels, individual medicine and medicine package, leveraging a heterogeneous graph. A dual-level interaction aware graph convolutional network is utilized to capture semantic information in the medical heterogeneous graph. Additionally, we incorporate medication indications into the medical heterogeneous graph as medicine commonsense knowledge. Extensive experimental results on real-world datasets validate the effectiveness of the proposed method.

The rapidly increasing adoption of Electronic Health Records (EHRs) has provided us with a comprehensive and reliable data repository, enabling the robust support for clinical decision making and the efficacious management of cardiovascular diseases. Patients may receive various treatment regimens due to the idiosyncratic dissimilarities in physical conditions. Hence, assigning specific category labels to treatment regimens for individual patients poses a challenge owing to the intricate nature of diverse medications and patient profiles. In this paper, we address this challenge by developing a novel model for Treatment Regimen Recommendations based on Siamese Neural Networks (TRRSNN). TRRSNN is harnessed to learn a global similarity metric from EHRs, facilitating the comparison and matching of target patients from unknown categories. The objective is to proffer recommendations of pre-existing treatments of patients that exhibit the highest semblance to those of target patients. Our proposed TRRSNN obviates the necessity for categorically labeling treatment regimens, and instead focuses on discerning the similarity among treatment regimens for pairwise patients. We conduct empirical evaluations on two real-world datasets, and the results demonstrate the superiority of our proposed TRRSNN over the baselines for treatment regimen recommendations.

Massive EHR (electronic health records) data contains rich health-related information and offers valuable opportunities for healthcare prediction tasks. Most existing works have achieved great success in improving the accuracy of prediction by leveraging deep learning to model sequential EHR data. However, entangled patient modeling results in inability of quantifying the importance of each type of feature. In this paper, we propose a feature-guided logical perception network FeLON for health risk prediction. FeLON is built on model-level explanation and consists of two main steps. The core part of the first step is a logical perception network to extract logical rules from patient features. In the second step, FeLON builds a fully-connected layer to integrate logical rules information to calculate health risk. Extensive experiments are conducted on two real disease datasets. The experimental results demonstrate that FeLON is able to maintain high prediction accuracy while bringing transparent reasoning process.

Clinical pathway recommendation aims to recommend a set of treatment modalities or treatment procedures for a patient. In order to achieve personalized clinical pathway recommendation, more and more researchers try to mine similar clinical paths from the statistical features of patient-related clinical data (such as electronic health records), while ignoring the high-order interactions between patient-related medical entities, and the time-series change pattern of this high-order interaction relationship, resulting in the inability of existing methods to fully describe patient characteristics and to accurately recommend personalized clinical pathways. To solve the above problems, we propose a new personalized clinical pathway recommendation model TempHRec. To model the complex high-order relationship in clinical pathway, hypergraph technology is introduced to solve the problem that clinical events are correlated at the same time window. On this basis, we propose a temporal hypergraph, to construct a hypergraph for each timestamp with the help of a sliding time window to capture the timing information at the clinical pathway. Extensive experimental results on real-world datasets show that the proposed model achieves the best results compared to baseline methods.