AIMC Topic: Vital Signs

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Predicting the Need For Vasopressors in the Intensive Care Unit Using an Attention Based Deep Learning Model.

Shock (Augusta, Ga.) Jul 1, 2021
BACKGROUND: Previous models on prediction of shock mostly focused on septic shock and often required laboratory results in their models. The purpose of this study was to use deep learning approaches to predict vasopressor requirement for critically i...
Humans Retrospective Studies Critical Illness Intensive Care Units Cohort Studies Models, Theoretical Vasoconstrictor Agents Vital Signs Needs Assessment Female Aged Male Middle Aged Deep Learning
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A Time-Phased Machine Learning Model for Real-Time Prediction of Sepsis in Critical Care.

Critical care medicine Oct 1, 2020
OBJECTIVES: As a life-threatening condition, sepsis is one of the major public health issues worldwide. Early prediction can improve sepsis outcomes with appropriate interventions. With the PhysioNet/Computing in Cardiology Challenge 2019, we aimed t...
Retrospective Studies Intensive Care Units Reproducibility of Results Severity of Illness Index Sepsis Humans Machine Learning Time Factors Critical Care Early Diagnosis Vital Signs
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Machine Learning Classifier Models Can Identify Acute Respiratory Distress Syndrome Phenotypes Using Readily Available Clinical Data.

American journal of respiratory and critical care medicine Oct 1, 2020
Two distinct phenotypes of acute respiratory distress syndrome (ARDS) with differential clinical outcomes and responses to randomly assigned treatment have consistently been identified in randomized controlled trial cohorts using latent class analys...
Machine Learning Phenotype Interleukin-6 Prognosis Biomarkers, Tumor Humans Protein C Mortality Bicarbonates Leukocyte Count Receptors, Tumor Necrosis Factor, Type I Partial Pressure Intercellular Adhesion Molecule-1 Creatinine Vasoconstrictor Agents Carbon Dioxide Plasminogen Activator Inhibitor 1 Platelet Count Bilirubin Pulmonary Ventilation Vital Signs Serum Albumin Latent Class Analysis Tidal Volume Inflammation Blood Pressure Age Factors Area Under Curve Respiratory Distress Syndrome Interleukin-8 Randomized Controlled Trials as Topic Oxygen
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Adding Continuous Vital Sign Information to Static Clinical Data Improves the Prediction of Length of Stay After Intubation: A Data-Driven Machine Learning Approach.

Respiratory care Sep 1, 2020
BACKGROUND: Bedside monitors in the ICU routinely measure and collect patients' physiologic data in real time to continuously assess the health status of patients who are critically ill. With the advent of increased computational power and the abilit...
Length of Stay Vital Signs Intubation, Intratracheal Humans Machine Learning Retrospective Studies
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Combat medic testing of a novel monitoring capability for early detection of hemorrhage.

The journal of trauma and acute care surgery Aug 1, 2020
BACKGROUND: Current out-of-hospital protocols to determine hemorrhagic shock in civilian trauma systems rely on standard vital signs with military guidelines relying on heart rate and strength of the radial pulse on palpation, all of which have prove...
Hemorrhage Military Personnel Early Diagnosis Vital Signs Blood Volume Military Medicine War-Related Injuries Humans Machine Learning Algorithms
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Validating clinical threshold values for a dashboard view of the compensatory reserve measurement for hemorrhage detection.

The journal of trauma and acute care surgery Aug 1, 2020
BACKGROUND: Compensatory reserve measurement (CRM) is a novel noninvasive monitoring technology designed to assess physiologic reserve using feature interrogation of arterial pulse waveforms. This study was conducted to validate clinically relevant C...
Blood Transfusion Hemorrhage Shock Injury Severity Score Vital Signs Blood Volume Data Display Middle Aged Hemodynamics User-Computer Interface ROC Curve Logistic Models Risk Factors Adult Sensitivity and Specificity Male Humans Machine Learning Female
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Detecting Patient Deterioration Using Artificial Intelligence in a Rapid Response System.

Critical care medicine Apr 1, 2020
OBJECTIVES: As the performance of a conventional track and trigger system in a rapid response system has been unsatisfactory, we developed and implemented an artificial intelligence for predicting in-hospital cardiac arrest, denoted the deep learning...
Humans Artificial Intelligence Algorithms Retrospective Studies Critical Illness Female Adult Male Clinical Deterioration Hospital Rapid Response Team Middle Aged Risk Assessment ROC Curve Vital Signs Heart Arrest
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Predicting Acute Graft-Versus-Host Disease Using Machine Learning and Longitudinal Vital Sign Data From Electronic Health Records.

JCO clinical cancer informatics Feb 1, 2020
PURPOSE: Acute graft-versus-host disease (aGVHD) remains a significant complication of allogeneic hematopoietic cell transplantation (HCT) and limits its broader application. The ability to predict grade II to IV aGVHD could potentially mitigate morb...
Transplantation, Homologous Vital Signs ROC Curve Longitudinal Studies Follow-Up Studies Graft vs Host Disease Humans Hematopoietic Stem Cell Transplantation Middle Aged Machine Learning Infant Biomarkers Adolescent Young Adult Child, Preschool Prognosis Adult Child Retrospective Studies Survival Rate Electronic Health Records Hematologic Neoplasms Female Male Aged
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A Machine Learning-Based Triage Tool for Children With Acute Infection in a Low Resource Setting.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies Dec 1, 2019
OBJECTIVES: To deploy machine learning tools (random forests) to develop a model that reliably predicts hospital mortality in children with acute infections residing in low- and middle-income countries, using age and other variables collected at hosp...
Infections Vital Signs Age Factors Triage Prognosis Humans Developing Countries Child Mortality Female Male Machine Learning Sex Factors Infant Prospective Studies Severity of Illness Index Adolescent Rwanda Child, Preschool Child Hospital Mortality
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Identifying Patients with Significant Problems Related to Social Determinants of Health with Natural Language Processing.

Studies in health technology and informatics Aug 21, 2019
Social and behavioral factors influence health but are infrequently recorded in electronic health records (EHRs). Here, we demonstrate that psychosocial vital signs can be extracted from EHR data. We processed structured and unstructured EHR data usi...
Vital Signs Humans Records Electronic Health Records Natural Language Processing Social Determinants of Health
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