Time Series Representation Learning π
Time Series Representation Learning π
Embedded Files
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Contrastive learning for EHR
Supervised contrastive learning
Deep representation learning for EHR
Prognostic monitoring system for shock in ICU patients
Temporal phenotyping of disease progression
Multivariate time series clustering
Early warning system for clinical deterioration
Patient subtyping for septic shock progression
Multi-task learning for continuous risk prediction
Multi-task learning prediction model
Model optimization for MTL
Continuous early warning system for acute kidney injury
EHR Multimodal Learning ππ§¬π
EHR Multimodal Learning ππ§¬π
Multimodal fusion of EHR
Robust representation learning from incomplete multimodal clinical data
Cross-modal temporal alignment
Online risk monitoring framework for pulmonary diseases
Wearable IoT-enabled health monitoring system
Deep representation learning for EHR
Time series clustering of cardiac rehabilitation recovery pattern
Wearable monitoring device
Reinforcement Learning for CDSS π₯οΈ
Reinforcement Learning for CDSS π₯οΈ
Medical dead-ends for safe RL
Safe reinforcement learning
Multi-agent deep reinforcement learning
Identifying high risk states and management of nephrotoxic drugs
Multimodal RL for embedding networks
Deep reinforcement learning
Multimodal data integration
Medication recommendation for Parkinsons' disease
Data-driven identification of drug-ADR relationships
Deep reinforcement learning
Network analysis and graph theory
Identifying nephrotoxic drug-drug interactions
Language Models for EHR π¬
Language Models for EHR π¬
Irregularly sampled time series in EHR
Inference of missing vital sign values using a pre-trained LLM
Lab result embedding based on temporal similarity between observed time and overall time
Deriving feature importance through variable self-attention
EHR sequence language modeling
Pretrained language model
Long-document transformer for EHR
EHR sequence representation for enhanced predictive modeling
Time Series Anomaly Detection π
Time Series Anomaly Detection π
Sequential anomaly detection for clinical deterioration
Time series anomaly detection
Early warning system for clinical deterioration
Detection of unexpected ICU admissions from ED
π₯ Medical Partners π§βπ€βπ§
π₯ Medical Partners π§βπ€βπ§
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