[HTML][HTML] Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies
Objective Temporal electronic health records (EHRs) contain a wealth of information for
secondary uses, such as clinical events prediction and chronic disease management …
secondary uses, such as clinical events prediction and chronic disease management …
A review of deep learning models and online healthcare databases for electronic health records and their use for health prediction
A fundamental obstacle to healthcare transformation continues to be the acquisition of
knowledge and insightful data from complex, high dimensional, and heterogeneous …
knowledge and insightful data from complex, high dimensional, and heterogeneous …
Gamenet: Graph augmented memory networks for recommending medication combination
Recent progress in deep learning is revolutionizing the healthcare domain including
providing solutions to medication recommendations, especially recommending medication …
providing solutions to medication recommendations, especially recommending medication …
Conditional generation net for medication recommendation
Medication recommendation targets to provide a proper set of medicines according to
patients' diagnoses, which is a critical task in clinics. Currently, the recommendation is …
patients' diagnoses, which is a critical task in clinics. Currently, the recommendation is …
Safedrug: Dual molecular graph encoders for recommending effective and safe drug combinations
Medication recommendation is an essential task of AI for healthcare. Existing works focused
on recommending drug combinations for patients with complex health conditions solely …
on recommending drug combinations for patients with complex health conditions solely …
Molerec: Combinatorial drug recommendation with substructure-aware molecular representation learning
Combinatorial drug recommendation involves recommending a personalized combination of
medication (drugs) to a patient over his/her longitudinal history, which essentially aims at …
medication (drugs) to a patient over his/her longitudinal history, which essentially aims at …
Personalizing medication recommendation with a graph-based approach
The broad adoption of electronic health records (EHRs) has led to vast amounts of data
being accumulated on a patient's history, diagnosis, prescriptions, and lab tests. Advances …
being accumulated on a patient's history, diagnosis, prescriptions, and lab tests. Advances …
Debiased, longitudinal and coordinated drug recommendation through multi-visit clinic records
AI-empowered drug recommendation has become an important task in healthcare research
areas, which offers an additional perspective to assist human doctors with more accurate …
areas, which offers an additional perspective to assist human doctors with more accurate …
Change matters: Medication change prediction with recurrent residual networks
Deep learning is revolutionizing predictive healthcare, including recommending medications
to patients with complex health conditions. Existing approaches focus on predicting all …
to patients with complex health conditions. Existing approaches focus on predicting all …
Order-free medicine combination prediction with graph convolutional reinforcement learning
Medicine Combination Prediction (MCP) based on Electronic Health Record (EHR) can
assist doctors to prescribe medicines for complex patients. Previous studies on MCP either …
assist doctors to prescribe medicines for complex patients. Previous studies on MCP either …