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Found 9 papers, 4 papers with code
Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models
This paper presents a novel approach to simulating electronic health records (EHRs) using diffusion probabilistic models (DPMs).
Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIV
Clinical data usually cannot be freely distributed due to their highly confidential nature and this hampers the development of machine learning in the healthcare domain.
The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithms
This has hampered the development of reproducible and generalisable machine learning applications in health care.
BIG-bench Machine Learning
Generative Adversarial Network
+1
Synthetic Acute Hypotension and Sepsis Datasets Based on MIMIC-III and Published as Part of the Health Gym Project
These two synthetic datasets comprise vital signs, laboratory test results, administered fluid boluses and vasopressors for 3, 910 patients with acute hypotension and for 2, 164 patients with sepsis in the Intensive Care Unit (ICU).
Designing Less Forgetful Networks for Continual Learning
Neural networks usually excel in learning a single task.
Learning to Continually Learn Rapidly from Few and Noisy Data
Neural networks suffer from catastrophic forgetting and are unable to sequentially learn new tasks without guaranteed stationarity in data distribution.
Highway-Connection Classifier Networks for Plastic yet Stable Continual Learning
Catastrophic forgetting occurs when a neural network is trained sequentially on multiple tasks – its weights will be continuously modified and as a result, the network will lose its ability in solving a previous task.
MTL2L: A Context Aware Neural Optimiser
Learning to learn (L2L) trains a meta-learner to assist the learning of a task-specific base learner.
EINS: Long Short-Term Memory with Extrapolated Input Network Simplification
This paper contrasts the two canonical recurrent neural networks (RNNs) of long short-term memory (LSTM) and gated recurrent unit (GRU) to propose our novel light-weight RNN of Extrapolated Input for Network Simplification (EINS).
