Medication recommendation systems have emerged as crucial tools in healthcare, offering personalized and effective drug combinations tailored to individual patient's clinical profiles. However, most existing approaches primarily focus on drug prediction by analyzing patient-drug interactions, often neglecting the intricate correlations between diseases and drugs. To address above limitation, this paper proposes a novel Collaborative Relation augmentation with Hierarchical Prescription inference network (CRHP) for effective medication recommendation. CRHP first constructs multiple covariance knowledge graphs to capture fine-grained interaction relationships between different entities from a global perspective. Based on self-built knowledge graphs, CRHP designs a collaborative relation augmented learning module, which introduces hypergraph convolutional networks to capture high-order association information between different entities. Moreover, CRHP devises a hierarchical prescription inference module that formulates drug prescriptions based on both current and historical patient information. The extensive experiments on two publicly available real-world medical datasets, MIMIC-III and MIMIC-IV, demonstrate the effectiveness of CRHP. The results indicate significant performance improvements over baseline methods, with gains of 2.12 and 1.31 in Jaccard, 1.91 and 1.83 in PRAUC, and 1.79 and 0.98 in F1-score (in percentage points).

As an effective and low-risk approach to identify new therapeutic pathways for existing drugs, drug repositioning has been extensively utilized to expedit drug discovery processes. However, current knowledge graph (KG)-based methodologies encounter several hurdles in this context. Firstly, most graph neural network (GNN)-based approaches fail to adequately capture the intricate relationships between drug-drug, drug-disease, or disease-disease. Secondly, the subtle synergistic mechanisms between drugs and diseases remain underexplored. Lastly, the training of knowledge graph embedding (KGE) methods is susceptible to noise, leading to unstable model optimization. To address these challenges, we intruduce KRANE, a knowledge-driven and relation-aware synergistic learning method for drug repositioning. KRANE addresses these issues through three innovative modules. Firstly, we design a relation-aware feature extractor (RAFE), which utilizes the contextual triples attention scores in KG to effectively integrate drug-related knowledge and enhance the representation of complex relational features. Secondly, we adopt a synergistic feature reconstruction module as a decoder to extract synergistic heterogeneous feature interactions between drugs and diseases from entity and relation representations. Finally, we propose a knowledge-regulated loss function to mitigate the impact of noise on model training. Experiments conducted on three publicly available datasets demonstrate that KRANE significantly outperforms existing methods.

Recent advancements in radiology report generation have utilized deep neural networks such as CNNs and Transformers, achieving notable improvements in generating accurate and detailed reports. However, their practical adoption is hindered by the challenge of balancing global dependency modeling with computational efficiency. The state space model, particularly its enhanced variant Mamba, offers promising linear-complexity solutions for long-range dependency modeling. Despite its strengths, Mamba’s fixed positional encoding limits its ability to effectively capture complex spatial dependencies. To address this gap, we propose RRG-Mamba, an advanced framework for efficient radiology report generation. Within the RRGMamba, we enhance the vanilla Mamba by integrating rotary position encoding (RoPE), enabling dynamic modeling of relative positional information in visual feature sequences. Furthermore, we design a global dependency learning module to optimize long-range visual feature sequence modeling. Extensive experiments on publicly available datasets, including IU X-Ray and MIMIC-CXR, demonstrate that RRG-Mamba achieves a 3.7% improvement in BLEU-4 score over existing models, along with significant gains in computational and memory efficiency. Our code is available at https://github.com/Eleanorhxd/RRG-Mamba.

Drug repositioning, which involves identifying new therapeutic indications for approved drugs, is pivotal in accelerating drug discovery. Recently, to mitigate the effect of label sparsity on inferring potential drug-disease associations (DDAs), graph contrastive learning (GCL) has emerged as a promising paradigm to supplement high-quality self-supervised signals through designing auxiliary tasks, then transfer shareable knowledge to main task, i.e. DDA prediction. However, existing approaches still encounter two limitations. The first is how to generate augmented views for fully capturing higher-order interaction semantics. The second is the optimization imbalance issue between auxiliary and main tasks. In this paper, we propose a novel heterogeneous Graph Contrastive learning method with Gradient Balance for DDA prediction, namely GCGB. To handle the first challenge, a fusion view is introduced to integrate both semantic views (drug and disease similarity networks) and interaction view (heterogeneous biomedical network). Next, inter-view contrastive learning auxiliary tasks are designed to contrast the fusion view with semantic and interaction views, respectively. For the second challenge, we adaptively adjust the gradient of GCL auxiliary tasks from the perspective of gradient direction and magnitude for better guiding parameter update toward main task. Extensive experiments conducted on three benchmarks under 10-fold cross-validation demonstrate the model effectiveness.

MOTIVATION: Drug recommendation aims to allocate safe and effective drug combinations based on the patient's health status from electronic health records, which is crucial to assist clinical physicians in making decisions. However, the existing drug recommendation works face two key challenges: (i) difficulty in fully representing the patient's health status leads to biased drug representation; (ii) only focusing on diagnostic representations of multiple visits, neglecting the modeling of patient drug history. RESULTS: To address the above limitations, we propose a multi-view gating retrieval network (MGRN) for robust drug recommendation. We design visit-, sequence-, and token-level views to provide different perspectives on the interaction between patients and drugs, obtaining a more comprehensive representation of drugs. Moreover, we develop a gating drug retrieval module to capture critical drug information from multiple visits, which can assist in recommending more reasonable drug combinations for the current visit. When evaluated on publicly real-world MIMIC-III and MIMIC-IV datasets, the proposed MGRN establishes a new benchmark performance, particularly achieving improvements of 1.36%, 1.71%, 1.21% and 2.12%, 2.36%, 1.81% in Jaccard, PRAUC, and F1-score, respectively, compared to state-of-the-art models. AVAILABILITY AND IMPLEMENTATION: The code is available at: https://github.com/kyosen258/MGRN.git.

Electronic health records (EHRs) comprehensively describe the health status of many patients during their visits. Combining the records in EHRs with the patient’s current medical treatment status can generate personalized medication combinations. However, the increasing number of drugs poses significant challenges for clinical experts to recommend combination drugs. In recent years, deep learning models have been widely studied and applied in drug recommendation task. Currently, the existing models either lack sufficient mining of patients’ health data or ignore the modelling of patients’ longitudinal medical information. Therefore, we propose a multi-visit interactive recalibration network (MIRNet) for drug recommendation with a triple graph encoder. Specifically, we design a medical recalibration module to capture the feature representations in patient diagnosis and procedure information through cascaded convolutions. To achieve friendly interaction of medical codes between relatively necessary historical visits and the current visit in the drug recommendation process, we propose a multi-visit filter module. Furthermore, we design a triple graph encoder to fuse molecular, EHR, and Drug-Drug Interation (DDI) graphs, which aims to extract implicit drug feature representations from different medical knowledge. We perform experiments on the real-world MIMIC-III dataset, and the experimental results reveal that the model MIRNet outperforms other competitive baselines regarding major indicators.

Automated International Classification of Diseases (ICD) coding tasks are designed to assign diagnosis and procedure codes to patients’ electronic medical records (EMRs). Recent works have applied deep neural network models and related techniques for code assignment to clinical notes. However, most existing methods have overlooked the advantageous complementary information presented in the tabular data from EMRs and the Wikipedia knowledge. Therefore, we propose a Multimodal Knowledge Fusion Network (MKFN) to effectively integrate clinical notes, tabular data, and Wikipedia knowledge, enhancing the model’s predictive capabilities. We incorporate structured tabular data and clinical notes into an initial multimodal representation using label attention and self-attention mechanisms. We propose a knowledge fusion network to leverage tabular data and Wikipedia knowledge for accurate predictions when code descriptions are absent in clinical notes. Experiments on the MIMIC dataset show that our proposed model achieves competitive results among existing ICD coding methods.