AIMC Topic: Shock, Septic

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Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
Sepsis, a life-threatening organ dysfunction, is a clinical syndrome triggered by acute infection and affects over 1 million Americans every year. Untreated sepsis can progress to septic shock and organ failure, making sepsis one of the leading cause...
Deep Learning Sepsis Decision Support Systems, Clinical Shock, Septic Natural Language Processing Clinical Decision Rules Humans Algorithms Electronic Health Records Language Severity of Illness Index
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Machine learning methods to improve bedside fluid responsiveness prediction in severe sepsis or septic shock: an observational study.

British journal of anaesthesia Jan 16, 2021
BACKGROUND: Passive leg raising (PLR) predicts fluid responsiveness in critical illness, although restrictions in mobilising patients often preclude this haemodynamic challenge being used. We investigated whether machine learning applied on transthor...
Fluid Therapy Critical Illness Echocardiography Humans Middle Aged Female Machine Learning Sepsis Aged Prospective Studies Shock, Septic Male Predictive Value of Tests Point-of-Care Systems
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Early detection of sepsis using artificial intelligence: a scoping review protocol.

Systematic reviews Jan 16, 2021
BACKGROUND: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. To decrease the high case fatality rates and morbidity for sepsis and septic shock, there is a need to increase the accuracy of early dete...
Research Design Shock, Septic Publications Population Groups Humans Scoping Reviews As Topic Artificial Intelligence
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Predicting Progression to Septic Shock in the Emergency Department Using an Externally Generalizable Machine-Learning Algorithm.

Annals of emergency medicine Jan 15, 2021
STUDY OBJECTIVE: Machine-learning algorithms allow improved prediction of sepsis syndromes in the emergency department (ED), using data from electronic medical records. Transfer learning, a new subfield of machine learning, allows generalizability of...
Female Risk Factors Aged Male Middle Aged Humans Artificial Intelligence Retrospective Studies Cohort Studies Shock, Septic Emergency Service, Hospital
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Assessment of Machine Learning to Estimate the Individual Treatment Effect of Corticosteroids in Septic Shock.

JAMA network open Dec 1, 2020
IMPORTANCE: The survival benefit of corticosteroids in septic shock remains uncertain.
Female Aged Male Glucocorticoids Shock, Septic Humans Machine Learning Prognosis Intensive Care Units Clinical Decision-Making Organ Dysfunction Scores Fludrocortisone Randomized Controlled Trials as Topic Hydrocortisone Critical Care Risk Adjustment
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Machine Learning to Support Hemodynamic Intervention in the Neonatal Intensive Care Unit.

Clinics in perinatology May 20, 2020
Hemodynamic support in neonatal intensive care is directed at maintaining cardiovascular wellbeing. At present, monitoring of vital signs plays an essential role in augmenting care in a reactive manner. By applying machine learning techniques, a mode...
Homeostasis Cerebrovascular Circulation Hemodynamic Monitoring Intensive Care Units, Neonatal Cardiovascular Physiological Phenomena Shock, Septic Infant, Premature Neonatal Sepsis Diagnostic Techniques, Cardiovascular Humans Machine Learning Infant, Newborn Cardiovascular Diseases
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A six‑gene support vector machine classifier contributes to the diagnosis of pediatric septic shock.

Molecular medicine reports Jan 23, 2020
Septic shock is induced by an uncontrolled inflammatory immune response to pathogens and the survival rate of patients with pediatric septic shock (PSS) is particularly low, with a mortality rate of 25‑50%. The present study explored the mechanisms o...
Humans Biomarkers Reproducibility of Results Infant Support Vector Machine Databases, Genetic Age Factors Gene Ontology ROC Curve Gene Expression Profiling Gene Regulatory Networks Adolescent Computational Biology Child, Preschool Gene Expression Regulation, Neoplastic Child Shock, Septic
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Automated screening of natural language in electronic health records for the diagnosis septic shock is feasible and outperforms an approach based on explicit administrative codes.

Journal of critical care Jan 9, 2020
PURPOSE: Identification of patients for epidemiologic research through administrative coding has important limitations. We investigated the feasibility of a search based on natural language processing (NLP) on the text sections of electronic health r...
Natural Language Processing Epidemiologic Studies Pattern Recognition, Automated False Positive Reactions International Classification of Diseases Sensitivity and Specificity Middle Aged Incidence Shock, Septic Predictive Value of Tests Female Hospitalization Young Adult Adult Humans Software Databases, Factual Prospective Studies Intensive Care Units Electronic Health Records
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Right Dose Right Now: bedside data-driven personalized antibiotic dosing in severe sepsis and septic shock - rationale and design of a multicenter randomized controlled superiority trial.

Trials Dec 18, 2019
BACKGROUND: Antibiotic exposure is often inadequate in critically ill patients with severe sepsis or septic shock and this is associated with worse outcomes. Despite markedly altered and rapidly changing pharmacokinetics in these patients, guidelines...
Drug Monitoring Netherlands Humans Shock, Septic Kaplan-Meier Estimate Artificial Intelligence Treatment Outcome Equivalence Trials as Topic Research Design Female Dose-Response Relationship, Drug Anti-Bacterial Agents Multicenter Studies as Topic Intensive Care Units Randomized Controlled Trials as Topic Adult Male Severity of Illness Index Big Data
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Machine learning for prediction of septic shock at initial triage in emergency department.

Journal of critical care Oct 22, 2019
BACKGROUND: We hypothesized utilizing machine learning (ML) algorithms for screening septic shock in ED would provide better accuracy than qSOFA or MEWS.
Young Adult Adult Male Middle Aged Area Under Curve Prognosis Sepsis Humans Emergency Service, Hospital Electronic Health Records Hospital Mortality Machine Learning Republic of Korea Multivariate Analysis Shock, Septic Software Female Vital Signs Support Vector Machine Algorithms Aged Triage
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