AIMC Topic: Intensive Care Units

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Explainable Machine Learning Model for Predicting GI Bleed Mortality in the Intensive Care Unit.

The American journal of gastroenterology Oct 1, 2020
INTRODUCTION: Acute gastrointestinal (GI) bleed is a common reason for hospitalization with 2%-10% risk of mortality. In this study, we developed a machine learning (ML) model to calculate the risk of mortality in intensive care unit patients admitte...
Machine Learning Intensive Care Units Female Aged Humans ROC Curve Area Under Curve APACHE Gastrointestinal Hemorrhage Aged, 80 and over Male Middle Aged Risk Assessment
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Machine Learning Prognostic Models for Gastrointestinal Bleeding Using Electronic Health Record Data.

The American journal of gastroenterology Aug 1, 2020
Risk assessment tools for patients with gastrointestinal bleeding may be used for determining level of care and informing management decisions. Development of models that use data from electronic health records is an important step for future deploym...
Electronic Health Records Gastrointestinal Hemorrhage Humans Machine Learning Prognosis Retrospective Studies Intensive Care Units
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Advances in the rehabilitation of intensive care unit acquired weakness: A case report on the promising use of robotics and virtual reality coupled to physiotherapy.

Medicine Jul 10, 2020
INTRODUCTION: Traditional physiotherapy is currently the best approach to manage patients with intensive care unit acquired weakness (ICUAW). We report on a patient with ICUAW, who was provided with an intensive, in-patient regimen, that is, conventi...
Muscle Weakness Intensive Care Units Male Middle Aged Virtual Reality Robotics Combined Modality Therapy Physical Therapy Modalities Humans
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Predicting Length of Stay for Cardiovascular Hospitalizations in the Intensive Care Unit: Machine Learning Approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Predicting Cardiovascular Length of stay based hospitalization at the time of patients' admitting to the coronary care unit (CCU) or (cardiac intensive care units CICU) is deemed as a challenging task to hospital management systems globally. Recently...
Humans Machine Learning Intensive Care Units Electronic Health Records Length of Stay Coronary Care Units
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A machine learning method for automatic detection and classification of patient-ventilator asynchrony.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Patients suffering from respiratory failure are often put on assisted mechanical ventilation. Patient-ventilator asynchrony (PVA) can occur during mechanical ventilation, which cause damage to the lungs and has been linked to increased mortality in t...
Respiration, Artificial Ventilators, Mechanical Respiration Humans Machine Learning Intensive Care Units
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Predictive Modeling of Pressure Injury Risk in Patients Admitted to an Intensive Care Unit.

American journal of critical care : an official publication, American Association of Critical-Care Nurses Jul 1, 2020
BACKGROUND: Pressure injuries are an important problem in hospital care. Detecting the population at risk for pressure injuries is the first step in any preventive strategy. Available tools such as the Norton and Braden scales do not take into accoun...
Intensive Care Units Electronic Health Records Female Risk Factors Humans Machine Learning Adult Sensitivity and Specificity Male Middle Aged Aged Aged, 80 and over Young Adult APACHE Pressure Ulcer Risk Assessment Hospitals, University Adolescent Child Hemoglobins Data Mining Critical Care
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Computer vision's potential to improve health care.

Lancet (London, England) May 16, 2020
United States Humans Medicare Algorithms Electronic Health Records Video Recording Patient Harm Clinical Decision-Making Artificial Intelligence Computers Vision, Ocular Surgeons Delivery of Health Care Intensive Care Units
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Prediction of an Acute Hypotensive Episode During an ICU Hospitalization With a Super Learner Machine-Learning Algorithm.

Anesthesia and analgesia May 1, 2020
BACKGROUND: Acute hypotensive episodes (AHE), defined as a drop in the mean arterial pressure (MAP) <65 mm Hg lasting at least 5 consecutive minutes, are among the most critical events in the intensive care unit (ICU). They are known to be associated...
Humans Machine Learning Algorithms Intensive Care Units Female Hypotension Middle Aged Hospitalization Cohort Studies Aged Predictive Value of Tests Male Acute Disease
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On classifying sepsis heterogeneity in the ICU: insight using machine learning.

Journal of the American Medical Informatics Association : JAMIA Mar 1, 2020
OBJECTIVES: Current machine learning models aiming to predict sepsis from electronic health records (EHR) do not account 20 for the heterogeneity of the condition despite its emerging importance in prognosis and treatment. This work demonstrates the ...
Organ Dysfunction Scores Electronic Health Records Sensitivity and Specificity Humans Sepsis Machine Learning Diagnosis, Differential Intensive Care Units
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Temporal convolutional networks allow early prediction of events in critical care.

Journal of the American Medical Informatics Association : JAMIA Mar 1, 2020
OBJECTIVE: Clinical interventions and death in the intensive care unit (ICU) depend on complex patterns in patients' longitudinal data. We aim to anticipate these events earlier and more consistently so that staff can consider preemptive action.
Logistic Models Critical Care Aged, 80 and over Male Middle Aged Hospital Mortality Female Hospitalization Aged Machine Learning Humans Computer Simulation Neural Networks, Computer Prognosis Intensive Care Units
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