Pre-training in medical data: A survey
Medical data refers to health-related information associated with regular patient care or as
part of a clinical trial program. There are many categories of such data, such as clinical …
part of a clinical trial program. There are many categories of such data, such as clinical …
Deep learning for medication recommendation: a systematic survey
Making medication prescriptions in response to the patient's diagnosis is a challenging task.
The number of pharmaceutical companies, their inventory of medicines, and the …
The number of pharmaceutical companies, their inventory of medicines, and the …
Learn from relational correlations and periodic events for temporal knowledge graph reasoning
Reasoning on temporal knowledge graphs (TKGR), aiming to infer missing events along the
timeline, has been widely studied to alleviate incompleteness issues in TKG, which is …
timeline, has been widely studied to alleviate incompleteness issues in TKG, which is …
When moe meets llms: Parameter efficient fine-tuning for multi-task medical applications
The recent surge in Large Language Models (LLMs) has garnered significant attention
across numerous fields. Fine-tuning is often required to fit general LLMs for a specific …
across numerous fields. Fine-tuning is often required to fit general LLMs for a specific …
Moelora: An moe-based parameter efficient fine-tuning method for multi-task medical applications
The recent surge in the field of Large Language Models (LLMs) has gained significant
attention in numerous domains. In order to tailor an LLM to a specific domain such as a web …
attention in numerous domains. In order to tailor an LLM to a specific domain such as a web …
Learning from hierarchical structure of knowledge graph for recommendation
Knowledge graphs (KGs) can help enhance recommendations, especially for the data-
sparsity scenarios with limited user-item interaction data. Due to the strong power of …
sparsity scenarios with limited user-item interaction data. Due to the strong power of …
A survey on knowledge graph-based recommender systems
D Li, H Qu, J Wang - 2023 China Automation Congress (CAC), 2023 - ieeexplore.ieee.org
Recommender systems have emerged as indispensable tools for information filtering, and
the integration of knowledge graphs for auxiliary information is becoming an increasingly …
the integration of knowledge graphs for auxiliary information is becoming an increasingly …
Recommendation based on attributes and social relationships
Attributes are important auxiliary information for representing user and item features,
especially in data-sparse scenario, and can serve as their main source. However, different …
especially in data-sparse scenario, and can serve as their main source. However, different …
SHGCN: Socially enhanced heterogeneous graph convolutional network for multi-behavior prediction
In recent years, multi-behavior information has been utilized to address data sparsity and
cold-start issues. The general multi-behavior models capture multiple behaviors of users to …
cold-start issues. The general multi-behavior models capture multiple behaviors of users to …
Contrastive learning on medical intents for sequential prescription recommendation
A Hadizadeh Moghaddam… - Proceedings of the 33rd …, 2024 - dl.acm.org
Recent advancements in sequential modeling applied to Electronic Health Records (EHR)
have greatly influenced prescription recommender systems. While the recent literature on …
have greatly influenced prescription recommender systems. While the recent literature on …