AIMC Topic: Critical Illness

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Explainable artificial intelligence model to predict acute critical illness from electronic health records.

Nature communications Jul 31, 2020
Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Early clinical prediction is typically based on manually calculated screening metrics that simply weigh these pa...
Heart Rate Early Diagnosis Acute Lung Injury Artificial Intelligence Prognosis Acute Kidney Injury Critical Illness Electronic Health Records Sepsis ROC Curve Blood Pressure Area Under Curve Acute Disease Humans
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Prediction on critically ill patients: The role of "big data".

Journal of critical care Jul 23, 2020
Accurate outcome prediction in Intensive Care Units (ICUs) would allow for better treatment planning, risk adjustment of study populations, and overall improvements in patient care. In the past, prognostic models have focused on mortality using simpl...
Humans Machine Learning Delivery of Health Care Severity of Illness Index Length of Stay Prognosis Hospital Mortality Critical Illness Critical Care Intensive Care Units Electronic Health Records
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Early triage of critically ill COVID-19 patients using deep learning.

Nature communications Jul 15, 2020
The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk o...
Pandemics COVID-19 Prognosis Critical Illness SARS-CoV-2 Humans Coronavirus Infections Hospitalization Survival Analysis Risk Middle Aged Deep Learning Betacoronavirus Models, Theoretical Pneumonia, Viral Triage
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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...
Humans Machine Learning Retrospective Studies Critical Illness Intensive Care Units Anemia, Sickle Cell Early Diagnosis Female Hospitalization Adult Male Multiple Organ Failure
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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...
Hospital Mortality ROC Curve Area Under Curve Models, Biological Simplified Acute Physiology Score Data Analysis Male Algorithms Middle Aged Prognosis Risk Assessment Retrospective Studies Cohort Studies Critical Illness Intensive Care Units Electronic Health Records Female Humans Hospitalization Machine Learning Aged
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Using a machine learning approach to predict mortality in critically ill influenza patients: a cross-sectional retrospective multicentre study in Taiwan.

BMJ open Feb 25, 2020
OBJECTIVES: Current mortality prediction models used in the intensive care unit (ICU) have a limited role for specific diseases such as influenza, and we aimed to establish an explainable machine learning (ML) model for predicting mortality in critic...
Models, Theoretical Taiwan Influenza, Human Outcome Assessment, Health Care Humans Machine Learning Retrospective Studies Critical Illness Female Adult Cross-Sectional Studies Male Middle Aged Hospital Mortality
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Mixed-integer optimization approach to learning association rules for unplanned ICU transfer.

Artificial intelligence in medicine Jan 30, 2020
After admission to emergency department (ED), patients with critical illnesses are transferred to intensive care unit (ICU) due to unexpected clinical deterioration occurrence. Identifying such unplanned ICU transfers is urgently needed for medical p...
Humans Machine Learning Critical Illness Intensive Care Units Severity of Illness Index Time Factors Quality Improvement Logistic Models Patient Transfer Models, Theoretical Hospitals, Teaching Age Factors Emergency Service, Hospital
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Machine learning to predict 30-day quality-adjusted survival in critically ill patients with cancer.

Journal of critical care Nov 2, 2019
PURPOSE: To develop and compare the predictive performance of machine-learning algorithms to estimate the risk of quality-adjusted life year (QALY) lower than or equal to 30 days (30-day QALY).
Humans Probability Machine Learning Pattern Recognition, Automated False Positive Reactions Female Hospitals, Public Hospitalization Aged Adult Sensitivity and Specificity Quality of Life Algorithms ROC Curve Prospective Studies Decision Trees Prognosis Male Area Under Curve Critical Illness Middle Aged Signal Processing, Computer-Assisted Intensive Care Units Brazil Neoplasms
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Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records.

Journal of biomedical informatics Sep 24, 2019
Electronic medical records (EMRs) support the development of machine learning algorithms for predicting disease incidence, patient response to treatment, and other healthcare events. But so far most algorithms have been centralized, taking little acc...
Middle Aged Length of Stay Cluster Analysis Critical Illness Electronic Health Records Female Male Humans Machine Learning Algorithms
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Machine learning versus physicians' prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor.

Critical care (London, England) Aug 16, 2019
BACKGROUND: Early diagnosis of acute kidney injury (AKI) is a major challenge in the intensive care unit (ICU). The AKIpredictor is a set of machine-learning-based prediction models for AKI using routinely collected patient information, and accessibl...
Male ROC Curve Acute Kidney Injury Middle Aged Humans Critical Illness Area Under Curve Machine Learning Surveys and Questionnaires Female Physicians Clinical Competence Aged Prospective Studies Adult
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