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Found 10 papers, 1 papers with code
Generating Multi-label Discrete Patient Records using Generative Adversarial Networks
Access to electronic health record (EHR) data has motivated computational advances in medical research.
SLEEPNET: Automated Sleep Staging System via Deep Learning
Sleep disorders, such as sleep apnea, parasomnias, and hypersomnia, affect 50-70 million adults in the United States (Hillman et al., 2006).
HAMLET: Interpretable Human And Machine co-LEarning Technique
Although cEEG monitoring yields large volumes of data, labeling costs and difficulty make it hard to build a classifier.
RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data
With the improvement of medical data capturing, vast amount of continuous patient monitoring data, e. g., electrocardiogram (ECG), real-time vital signs and medications, become available for clinical decision support at intensive care units (ICUs).
Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment
We propose doctor2vec which simultaneously learns 1) doctor representations from EHR data and 2) trial representations from the description and categorical information about the trials.
CONAN: Complementary Pattern Augmentation for Rare Disease Detection
How to further leverage patients with possibly uncertain diagnosis to improve detection?
CLARA: Clinical Report Auto-completion
Most existing methods try to generate the whole reports from the raw input with limited success because 1) generated reports often contain errors that need manual review and correction, 2) it does not save time when doctors want to write additional information into the report, and 3) the generated reports are not customized based on individual doctors' preference.
EMIXER: End-to-end Multimodal X-ray Generation via Self-supervision
EMIXER is an conditional generative adversarial model by 1) generating an image based on a label, 2) encoding the image to a hidden embedding, 3) producing the corresponding text via a hierarchical decoder from the image embedding, and 4) a joint discriminator for assessing both the image and the corresponding text.
EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders
De-identified EHRs do not adequately address the needs of health systems, as de-identified data are susceptible to re-identification and its volume is also limited.
Automated Respiratory Event Detection Using Deep Neural Networks
Using 9, 656 polysomnography recordings from the Massachusetts General Hospital (MGH), we trained a neural network (WaveNet) based on a single respiratory effort belt to detect obstructive apnea, central apnea, hypopnea and respiratory-effort related arousals.
