AIMC Topic: Sepsis

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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.
Middle Aged Sepsis Hospital Mortality Shock, Septic Support Vector Machine Aged Young Adult Adult Male Triage Humans Area Under Curve Machine Learning Emergency Service, Hospital Software Multivariate Analysis Vital Signs Algorithms Prognosis Electronic Health Records Republic of Korea Female
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Clinical applications of artificial intelligence in sepsis: A narrative review.

Computers in biology and medicine Oct 7, 2019
Many studies have been published on a variety of clinical applications of artificial intelligence (AI) for sepsis, while there is no overview of the literature. The aim of this review is to give an overview of the literature and thereby identify know...
Humans Machine Learning Anti-Bacterial Agents Sepsis Models, Biological
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Leveraging implicit expert knowledge for non-circular machine learning in sepsis prediction.

Artificial intelligence in medicine Sep 24, 2019
Sepsis is the leading cause of death in non-coronary intensive care units. Moreover, a delay of antibiotic treatment of patients with severe sepsis by only few hours is associated with increased mortality. This insight makes accurate models for early...
Humans Machine Learning Diagnosis, Computer-Assisted Surveys and Questionnaires Sepsis Models, Statistical Observer Variation Intensive Care Units Female Male Middle Aged
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Machine learning for clinical decision support in infectious diseases: a narrative review of current applications.

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases Sep 17, 2019
BACKGROUND: Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID).
Patient Outcome Assessment Anti-Infective Agents Humans Clinical Decision-Making Artificial Intelligence Sepsis Machine Learning Decision Support Systems, Clinical Communicable Diseases Early Diagnosis
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Predicting sepsis with a recurrent neural network using the MIMIC III database.

Computers in biology and medicine Aug 20, 2019
OBJECTIVE: Predicting sepsis onset with a recurrent neural network and performance comparison with InSight - a previously proposed algorithm for the prediction of sepsis onset.
Middle Aged Diagnosis, Computer-Assisted Sepsis Databases, Factual Female Adult Male Humans Neural Networks, Computer Machine Learning
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An intelligent warning model for early prediction of cardiac arrest in sepsis patients.

Computer methods and programs in biomedicine Jun 11, 2019
BACKGROUND: Sepsis-associated cardiac arrest is a common issue with the low survival rate. Early prediction of cardiac arrest can provide the time required for intervening and preventing its onset in order to reduce mortality. Several studies have be...
Monitoring, Ambulatory APACHE Multivariate Analysis Vital Signs Normal Distribution Heart Arrest Case-Control Studies Sepsis Adolescent Incidence Decision Trees Sensitivity and Specificity Male Humans Middle Aged Machine Learning Severity of Illness Index Algorithms Electronic Health Records Female Reproducibility of Results Young Adult Adult
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A machine learning approach for predicting urine output after fluid administration.

Computer methods and programs in biomedicine May 13, 2019
BACKGROUND AND OBJECTIVE: To develop a machine learning model to predict urine output (UO) in sepsis patients after fluid resuscitation.
Humans Machine Learning Algorithms Intensive Care Units Electronic Health Records Female Risk Factors Aged Sensitivity and Specificity Resuscitation Kidney Function Tests Male Area Under Curve Middle Aged Fluid Therapy Sepsis Critical Care Decision Support Systems, Clinical Urination
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Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs.

Computers in biology and medicine Apr 24, 2019
OBJECTIVE: Sepsis remains a costly and prevalent syndrome in hospitals; however, machine learning systems can increase timely sepsis detection using electronic health records. This study validates a gradient boosted ensemble machine learning tool for...
Severity of Illness Index Diagnosis, Computer-Assisted Machine Learning Sepsis Databases, Factual Intensive Care Units Adolescent Electronic Health Records Vital Signs Female Humans Aged Adult Male Middle Aged
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Refining humane endpoints in mouse models of disease by systematic review and machine learning-based endpoint definition.

ALTEX Apr 18, 2019
Ideally, humane endpoints allow for early termination of experiments by minimizing an animal's discomfort, distress and pain, while ensuring that scientific objectives are reached. Yet, lack of commonly agreed methodology and heterogeneity of cut-off...
Mice, Knockout Sepsis Female Systematic Reviews as Topic Supervised Machine Learning Animals Time Factors Stroke Male Animal Welfare Mice Body Weight Severity of Illness Index Random Allocation Disease Models, Animal Body Temperature Mice, Inbred C57BL Ethical Review
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Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data.

PloS one Feb 22, 2019
BACKGROUND: Rapid antibiotic administration is known to improve sepsis outcomes, however early diagnosis remains challenging due to complex presentation. Our objective was to develop a model using readily available electronic health record (EHR) data...
Female Infant, Newborn Male Infant Sepsis Humans Diagnosis, Computer-Assisted Models, Biological Machine Learning Critical Care Retrospective Studies Electronic Health Records
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