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Found 10 papers, 6 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).
Incorporating Uncertainty in Learning to Defer Algorithms for Safe Computer-Aided Diagnosis
LDU was evaluated on the diagnosis of myocardial infarction (using discharge summaries), the diagnosis of any comorbidities (using structured data), and the diagnosis of pleural effusion and pneumothorax (using chest x-rays), and compared with 'learning to defer without uncertainty information' (LD) and 'direct triage by uncertainty' (DT) methods.
Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach
After excluding people with prior CVD or heart failure, sex-specific deep learning and CPH models were developed to estimate the risk of fatal or non-fatal CVD events within five years.
Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients
IVIM-NET$_{optim}$ showed superior performance to the LS and Bayesian approaches at SNRs<50.
Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk
Methods: Several deep learning architectures making use of attention mechanisms, recurrent layers, neural ordinary differential equations (ODEs), and medical concept embeddings with time-aware attention were trained using publicly available electronic medical record data (MIMIC-III) associated with 45, 298 ICU stays for 33, 150 patients.
Deep Learning How to Fit an Intravoxel Incoherent Motion Model to Diffusion-Weighted MRI
This approach was associated with high consistency between the two readers (ICCs between 50 and 97%), low intersubject variability of estimated parameter values (CVs between 9. 2 and 28. 4), and the lowest error when compared with least-squares and Bayesian approaches.
A Ray-based Approach for Boundary Estimation of Fiber Bundles Derived from Diffusion Tensor Imaging
Using DTI data, fiber bundles can be determined, to gain information about eloquent brain structures.
