AIMC Topic: Intensive Care Units

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The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit.

International journal of medical informatics May 21, 2020
BACKGROUND: Severe sepsis and septic shock are still the leading causes of death in Intensive Care Units (ICUs), and timely diagnosis is crucial for treatment outcomes. The progression of electronic medical records (EMR) offers the possibility of sto...
Cohort Studies ROC Curve Aged Male Sepsis Humans Artificial Intelligence Algorithms Prospective Studies Prognosis Retrospective Studies Intensive Care Units Female
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Pulmonary Embolism in Acute Asthma Exacerbation: Clinical Characteristics, Prediction Model and Hospital Outcomes.

Lung May 18, 2020
PURPOSE: Little is known about the characteristics and impact of acute pulmonary embolism (PE) during episodes of asthma exacerbation. We aimed to characterize patients diagnosed with acute PE in the setting of asthma exacerbation, develop a predicti...
Humans Machine Learning Asthma Male Middle Aged Disease Progression Intensive Care Units Female Aged Adult Creatinine Body Mass Index Fibrin Fibrinogen Degradation Products Hospitals, University Comorbidity Computed Tomography Angiography Oxygen Heart Rate Case-Control Studies Clinical Decision Rules Natriuretic Peptide, Brain Length of Stay International Normalized Ratio Pulmonary Embolism
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Using Machine Learning to Predict Early Onset Acute Organ Failure in Critically Ill Intensive Care Unit Patients With Sickle Cell Disease: Retrospective Study.

Journal of medical Internet research May 13, 2020
BACKGROUND: Sickle cell disease (SCD) is a genetic disorder of the red blood cells, resulting in multiple acute and chronic complications, including pain episodes, stroke, and kidney disease. Patients with SCD develop chronic organ dysfunction, which...
Female Hospitalization Adult Humans Male Multiple Organ Failure Machine Learning Anemia, Sickle Cell Retrospective Studies Early Diagnosis Critical Illness Intensive Care Units
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Machine learning in intensive care medicine: ready for take-off?

Intensive care medicine May 12, 2020
Machine Learning Medicine Intensive Care Units Critical Care Humans
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Predicting severe clinical events by learning about life-saving actions and outcomes using distant supervision.

Journal of biomedical informatics Apr 26, 2020
Medical error is a leading cause of patient death in the United States. Among the different types of medical errors, harm to patients caused by doctors missing early signs of deterioration is especially challenging to address due to the heterogeneity...
Machine Learning Acute Kidney Injury Intensive Care Units Hospitalization Humans
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LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data.

PloS one Apr 17, 2020
Intensive care data are valuable for improvement of health care, policy making and many other purposes. Vast amount of such data are stored in different locations, on many different devices and in different data silos. Sharing data among different so...
Humans Machine Learning Databases, Factual Intensive Care Units Medical Informatics
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Artificial Intelligence in the Intensive Care Unit.

Critical care (London, England) Mar 24, 2020
This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2020. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2020. Further information about the...
Decision Support Techniques Intensive Care Units Severity of Illness Index Humans Artificial Intelligence
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Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records.

The Lancet. Digital health Mar 12, 2020
BACKGROUND: Many mortality prediction models have been developed for patients in intensive care units (ICUs); most are based on data available at ICU admission. We investigated whether machine learning methods using analyses of time-series data impro...
Humans Machine Learning Data Analysis Models, Biological Simplified Acute Physiology Score Aged Intensive Care Units Male Algorithms Electronic Health Records Middle Aged Cohort Studies Prognosis Female Risk Assessment Hospital Mortality Retrospective Studies Hospitalization ROC Curve Critical Illness Area Under Curve
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Early prediction of circulatory failure in the intensive care unit using machine learning.

Nature medicine Mar 9, 2020
Intensive-care clinicians are presented with large quantities of measurements from multiple monitoring systems. The limited ability of humans to process complex information hinders early recognition of patient deterioration, and high numbers of monit...
ROC Curve Models, Theoretical Shock Databases as Topic Risk Factors Cohort Studies Time Factors Humans Machine Learning Prognosis Intensive Care Units Reproducibility of Results
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A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections.

Nature communications Mar 4, 2020
Improved identification of bacterial and viral infections would reduce morbidity from sepsis, reduce antibiotic overuse, and lower healthcare costs. Here, we develop a generalizable host-gene-expression-based classifier for acute bacterial and viral ...
Bacterial Infections Hospital Mortality Aged Acute Disease Virus Diseases ROC Curve Humans Middle Aged Datasets as Topic Aged, 80 and over Neural Networks, Computer RNA, Messenger Gene Expression Profiling Host-Pathogen Interactions Adult Intensive Care Units Support Vector Machine Sepsis Male Female
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