Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study

Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package)

(2018) Maximilian Christ et al.   NEUROCOMPUTING

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

(2018) Rishikesan Kamaleswaran et al.   Pediatric Critical Care Medicine

A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier

(2018) Franco van Wyk et al.   INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS

Predictors of mortality in adults with Sickle cell disease admitted to intensive care unit in Bahrain

(2017) Sana Abdulaaziz Al Khawaja et al.   JOURNAL OF CRITICAL CARE

Sickle cell disease in the older adult

(2017) Mya S. Thein et al.   PATHOLOGY

Renal Failure in Sickle Cell Disease: Prevalence, Predictors of Disease, Mortality and Effect on Length of Hospital Stay

(2016) Sri L.H. Yeruva et al.   HEMOGLOBIN

The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)

(2016) Mervyn Singer et al.   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION

Machine Learning and Decision Support in Critical Care

(2016) Alistair E. W. Johnson et al.   PROCEEDINGS OF THE IEEE

Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions

(2012) Alekh Agarwal et al.   ANNALS OF STATISTICS

EEG signals classification using the K-means clustering and a multilayer perceptron neural network model

(2011) Umut Orhan et al.   EXPERT SYSTEMS WITH APPLICATIONS

SYMBOLIC ONE-CLASS LEARNING FROM IMBALANCED DATASETS: APPLICATION IN MEDICAL DIAGNOSIS

(2009) LUIS MENA et al.   International Journal on Artificial Intelligence Tools

A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification

(2008) Alexander Statnikov et al.   BMC BIOINFORMATICS