Machine learning and decision support in critical care
Clinical data management systems typically provide caregiver teams with useful information,
derived from large, sometimes highly heterogeneous, data sources that are often changing …
derived from large, sometimes highly heterogeneous, data sources that are often changing …
Improving detection of patient deterioration in the general hospital ward environment
Patient monitoring on low acuity general hospital wards is currently based largely on
intermittent observations and measurements of simple variables, such as blood pressure …
intermittent observations and measurements of simple variables, such as blood pressure …
Federated learning for healthcare informatics
With the rapid development of computer software and hardware technologies, more and
more healthcare data are becoming readily available from clinical institutions, patients …
more healthcare data are becoming readily available from clinical institutions, patients …
Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records
Background and objective Cardiac arrhythmia, which is an abnormal heart rhythm, is a
common clinical problem in cardiology. Detection of arrhythmia on an extended duration …
common clinical problem in cardiology. Detection of arrhythmia on an extended duration …
[HTML][HTML] State-of-the-art deep learning methods on electrocardiogram data: systematic review
Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic
tools that can provide useful information regarding a patient's health status. Deep learning …
tools that can provide useful information regarding a patient's health status. Deep learning …
Computational approaches to alleviate alarm fatigue in intensive care medicine: A systematic literature review
Patient monitoring technology has been used to guide therapy and alert staff when a vital
sign leaves a predefined range in the intensive care unit (ICU) for decades. However, large …
sign leaves a predefined range in the intensive care unit (ICU) for decades. However, large …
Fast online changepoint detection via functional pruning CUSUM statistics
Many modern applications of online changepoint detection require the ability to process
high-frequency observations, sometimes with limited available computational resources …
high-frequency observations, sometimes with limited available computational resources …
Towards better heartbeat segmentation with deep learning classification
The confidence of medical equipment is intimately related to false alarms. The higher the
number of false events occurs, the less truthful is the equipment. In this sense, reducing (or …
number of false events occurs, the less truthful is the equipment. In this sense, reducing (or …
[PDF][PDF] A Diffusion Model with Contrastive Learning for ICU False Arrhythmia Alarm Reduction.
The high rate of false arrhythmia alarms in intensive care units (ICUs) can negatively impact
patient care and lead to slow staff response time due to alarm fatigue. To reduce false …
patient care and lead to slow staff response time due to alarm fatigue. To reduce false …
Reduction of false arrhythmia alarms using signal selection and machine learning
In this paper, we propose an algorithm that classifies whether a generated cardiac
arrhythmia alarm is true or false. The large number of false alarms in intensive care is a …
arrhythmia alarm is true or false. The large number of false alarms in intensive care is a …