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Medication recommendation combines patient medical history with biomedical knowledge to assist doctors in determining medication combinations more accurately and safely. Existing works based on molecular knowledge neglect the 3D geometric structure of molecules and fail to learn the high-dimensional information of medications, leading to structural confusion. Additionally, it does not extract key substructures from a single patient visit, resulting in the failure to identify medication molecules suitable for the current patient visit. To address the above limitations, we propose a bimodal molecular recommendation framework named BiMoRec, which introduces 3D molecular structures to obtain atomic 3D coordinates and edge indices, overcoming the inherent lack of high-dimensional molecular information in 2D molecular structures. To retain the fast training and prediction efficiency of the recommendation system, we use bimodal graph contrastive pretraining to maximize the mutual information between the two molecular modalities, achieving the fusion of 2D and 3D molecular graphs and re-evaluating substructures at the visit level. Specifically, we use deep learning networks to construct a pretraining method that acquires 2D and 3D molecular structure representations and substructure representations, and obtain mutual information through contrastive learning. We then generate fused molecular representations using the trained GNN module and re-determine the relevance of substructure representations in combination with the patient's clinical history. Finally, we generate the final medication combination based on the extracted substructure sequences. Our implementation on the MIMIC-III and MIMIC-IV datasets demonstrates that our method achieves state-of-the-art performance. Compared to the second-best baseline, our model improves accuracy by 2.07%, with DDI at the same level as the baseline., Comment: 14 pages, 9 figures

Medication recommendation systems are developed to recommend suitable medications tailored to specific patient. Previous researches primarily focus on learning medication representations, which have yielded notable advances. However, these methods are limited to capturing personalized patient representations due to the following primary limitations: (i) unable to capture the differences in the impact of diseases/procedures on patients across various patient health states; (ii) fail to model the direct causal relationships between medications and specific health state of patients, resulting in an inability to determine which specific disease each medication is treating. To address these limitations, we propose CausalMed, a patient health state-centric model capable of enhancing the personalization of patient representations. Specifically, CausalMed first captures the causal relationship between diseases/procedures and medications through causal discovery and evaluates their causal effects. Building upon this, CausalMed focuses on analyzing the health state of patients, capturing the dynamic differences of diseases/procedures in different health states of patients, and transforming diseases/procedures into medications on direct causal relationships. Ultimately, CausalMed integrates information from longitudinal visits to recommend medication combinations. Extensive experiments on real-world datasets show that our method learns more personalized patient representation and outperforms state-of-the-art models in accuracy and safety., Comment: CIKM 2024 Full Research Paper

Multi-behavioral recommendation optimizes user experiences by providing users with more accurate choices based on their diverse behaviors, such as view, add to cart, and purchase. Current studies on multi-behavioral recommendation mainly explore the connections and differences between multi-behaviors from an implicit perspective. Specifically, they directly model those relations using black-box neural networks. In fact, users' interactions with items under different behaviors are driven by distinct intents. For instance, when users view products, they tend to pay greater attention to information such as ratings and brands. However, when it comes to the purchasing phase, users become more price-conscious. To tackle this challenge and data sparsity problem in the multi-behavioral recommendation, we propose a novel model: Knowledge-Aware Multi-Intent Contrastive Learning (KAMCL) model. This model uses relationships in the knowledge graph to construct intents, aiming to mine the connections between users' multi-behaviors from the perspective of intents to achieve more accurate recommendations. KAMCL is equipped with two contrastive learning schemes to alleviate the data scarcity problem and further enhance user representations. Extensive experiments on three real datasets demonstrate the superiority of our model.

Medication recommendation aims to integrate patients' long-term health records to provide accurate and safe medication combinations for specific health states. Existing methods often fail to deeply explore the true causal relationships between diseases/procedures and medications, resulting in biased recommendations. Additionally, in medication representation learning, the relationships between information at different granularities of medications, coarse-grained (medication itself) and fine-grained (molecular level), are not effectively integrated, leading to biases in representation learning. To address these limitations, we propose the Causal Inference-driven Dual-Granularity Medication Recommendation method (CIDGMed). Our approach leverages causal inference to uncover the relationships between diseases/procedures and medications, thereby enhancing the rationality and interpretability of recommendations. By integrating coarse-grained medication effects with fine-grained molecular structure information, CIDGMed provides a comprehensive representation of medications. Additionally, we employ a bias correction model during the prediction phase to further refine recommendations, ensuring both accuracy and safety. Through extensive experiments, CIDGMed significantly outperforms current state-of-the-art models across multiple metrics, achieving a 2.54% increase in accuracy, a 3.65% reduction in side effects, and a 39.42% improvement in time efficiency. Additionally, we demonstrate the rationale of CIDGMed through a case study.

With the growing imbalance between limited medical resources and escalating demands, AI-based clinical tasks have become paramount. Medication recommendation, as a sub-domain, aims to amalgamate longitudinal patient history with medical knowledge, assisting physicians in prescribing safer and more accurate medication combinations. Existing methods overlook the inherent long-tail distribution in medical data, lacking balanced representation between head and tail data, which leads to sub-optimal model performance. To address this challenge, we introduce StratMed, a model that incorporates an innovative relevance stratification mechanism. It harmonizes discrepancies in data long-tail distribution and strikes a balance between the safety and accuracy of medication combinations. Specifically, we first construct a pre-training method using deep learning networks to obtain entity representation. After that, we design a pyramid-like data stratification method to obtain more generalized entity relationships by reinforcing the features of unpopular entities. Based on this relationship, we designed two graph structures to express medication precision and safety at the same level to obtain visit representations. Finally, the patient's historical clinical information is fitted to generate medication combinations for the current health condition. Experiments on the MIMIC-III dataset demonstrate that our method has outperformed current state-of-the-art methods in four evaluation metrics (including safety and accuracy).