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Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the PICU.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
OBJECTIVES: We used artificial intelligence to develop a novel algorithm using physiomarkers to predict the onset of severe sepsis in critically ill children.

The impact of social housing on mental health: longitudinal analyses using marginal structural models and machine learning-generated weights.

International journal of epidemiology
BACKGROUND: Social housing may provide an affordable and secure residential environment, but has also been associated with stigma, poor housing conditions and locational disadvantage. We examined the cumulative effect of additional years, and tenure ...

Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis.

Anesthesiology
WHAT WE ALREADY KNOW ABOUT THIS TOPIC: WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: With appropriate algorithms, computers can learn to detect patterns and associations in large data sets. The authors' goal was to apply machine learning to ar...

Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality.

Anesthesiology
WHAT WE ALREADY KNOW ABOUT THIS TOPIC: WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: The authors tested the hypothesis that deep neural networks trained on intraoperative features can predict postoperative in-hospital mortality.

Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer's Disease Diagnosis.

Neuroinformatics
Accurate and early diagnosis of Alzheimer's disease (AD) plays important role for patient care and development of future treatment. Structural and functional neuroimages, such as magnetic resonance images (MRI) and positron emission tomography (PET),...

Proposing New RadLex Terms by Analyzing Free-Text Mammography Reports.

Journal of digital imaging
After years of development, the RadLex terminology contains a large set of controlled terms for the radiology domain, but gaps still exist. We developed a data-driven approach to discover new terms for RadLex by mining a large corpus of radiology rep...

Automated Pathogenesis-Based Diagnosis of Lumbar Neural Foraminal Stenosis via Deep Multiscale Multitask Learning.

Neuroinformatics
Pathogenesis-based diagnosis is a key step to prevent and control lumbar neural foraminal stenosis (LNFS). It conducts both early diagnosis and comprehensive assessment by drawing crucial pathological links between pathogenic factors and LNFS. Automa...

Unsupervised Learning of Spatiotemporal Interictal Discharges in Focal Epilepsy.

Neurosurgery
BACKGROUND: Interictal epileptiform discharges are an important biomarker for localization of focal epilepsy, especially in patients who undergo chronic intracranial monitoring. Manual detection of these pathophysiological events is cumbersome, but i...

Hemodynamic Instability and Cardiovascular Events After Traumatic Brain Injury Predict Outcome After Artifact Removal With Deep Belief Network Analysis.

Journal of neurosurgical anesthesiology
BACKGROUND: Hemodynamic instability and cardiovascular events heavily affect the prognosis of traumatic brain injury. Physiological signals are monitored to detect these events. However, the signals are often riddled with faulty readings, which jeopa...