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Found 12 papers, 5 papers with code
Benchmarking real-time algorithms for in-phase auditory stimulation of low amplitude slow waves with wearable EEG devices during sleep
Auditory stimulation of EEG slow waves (SW) during non-rapid eye movement (NREM) sleep has shown to improve cognitive function when it is delivered at the up-phase of SW. SW enhancement is particularly desirable in subjects with low-amplitude SW such as older adults or patients suffering from neurodegeneration such as Parkinson disease (PD).
Detect-and-Segment: a Deep Learning Approach to Automate Wound Image Segmentation
When tested on the wound images drawn from the supplemental data sets, the DS approach increased the mean MCC from 0. 17 to 0. 85.
Multispectral Video Fusion for Non-contact Monitoring of Respiratory Rate and Apnea
The algorithm independently addresses the RR estimation and apnea detection tasks.
A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data
Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms.
CXPlain: Causal Explanations for Model Interpretation under Uncertainty
Feature importance estimates that inform users about the degree to which given inputs influence the output of a predictive model are crucial for understanding, validating, and interpreting machine-learning models.
Forecasting intracranial hypertension using multi-scale waveform metrics
Approach: We developed a prediction framework that forecasts onsets of acute intracranial hypertension in the next 8 hours.
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy.
Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks
However, current methods for training neural networks for counterfactual inference on observational data are either overly complex, limited to settings with only two available treatments, or both.
PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data
One factor that contributes to misdiagnoses is that the symptoms of Parkinson's disease may not be prominent at the time the clinical assessment is performed.
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care
Patients in the intensive care unit (ICU) require constant and close supervision.
Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks
Knowledge of the importance of input features towards decisions made by machine-learning models is essential to increase our understanding of both the models and the underlying data.
Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks
With tens of thousands of electrocardiogram (ECG) records processed by mobile cardiac event recorders every day, heart rhythm classification algorithms are an important tool for the continuous monitoring of patients at risk.
