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Found 13 papers, 7 papers with code
Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data
Medical events of interest, such as mortality, often happen at a low rate in electronic medical records, as most admitted patients survive.
Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosis
Advances in self-supervised learning (SSL) have shown that self-supervised pretraining on medical imaging data can provide a strong initialization for downstream supervised classification and segmentation.
Predicting the meal macronutrient composition from continuous glucose monitors
We built a multitask neural network to estimate the macronutrient composition from the CGM signal, and compared it against a baseline linear regression.
Automated multilabel diagnosis on electrocardiographic images and signals
The application of artificial intelligence (AI) for automated diagnosis of electrocardiograms (ECGs) can improve care in remote settings but is limited by the reliance on infrequently available signal-based data.
Boosted-SpringDTW for Comprehensive Feature Extraction of Physiological Signals
Goal: To achieve-high quality comprehensive feature extraction from physiological signals that enables precise physiological parameter estimation despite evolving waveform morphologies.
Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards
Electronic Health Record (EHR) systems provide critical, rich and valuable information at high frequency.
BoXHED2.0: Scalable boosting of dynamic survival analysis
Modern applications of survival analysis increasingly involve time-dependent covariates.
Dynamically Extracting Outcome-Specific Problem Lists from Clinical Notes with Guided Multi-Headed Attention
We identify risk factors for both readmission and mortality outcomes and demonstrate that our framework can be used to develop dynamic problem lists that present clinical problems along with their quantitative importance.
Developing Personalized Models of Blood Pressure Estimation from Wearable Sensors Data Using Minimally-trained Domain Adversarial Neural Networks
A practical problem with this approach is that the amount of data required to confidently train such a regression model can be prohibitive.
BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates
BoXHED 1. 0 is a novel tree-based implementation of the generic estimator proposed in Lee, Chen, Ishwaran (2017), which was designed for handling time-dependent covariates in a fully nonparametric manner.
Explainable Prediction of Adverse Outcomes Using Clinical Notes
Clinical notes contain a large amount of clinically valuable information that is ignored in many clinical decision support systems due to the difficulty that comes with mining that information.
A Survey of Challenges and Opportunities in Sensing and Analytics for Cardiovascular Disorders
We highlight three primary needs in the design of new smart health technologies: 1) the need for sensing technology that can track longitudinal trends in signs and symptoms of the cardiovascular disorder despite potentially infrequent, noisy, or missing data measurements; 2) the need for new analytic techniques that model data captured in a longitudinal, continual fashion to aid in the development of new risk prediction techniques and in tracking disease progression; and 3) the need for machine learning techniques that are personalized and interpretable, allowing for advancements in shared clinical decision making.
Visualization of Emergency Department Clinical Data for Interpretable Patient Phenotyping
In the five chief complaints, we find between 2 and 6 clusters, with the peak mean pairwise ARI between subsequent training iterations to range from 0. 35 to 0. 74.
