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Sepsis

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Potential Prognostic Markers in the Heart Rate Variability Features for Early Diagnosis of Sepsis in the Pediatric Intensive Care Unit using Convolutional Neural Network Classifiers.

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
Blood infection due to different circumstances could immediately develop to an extreme body reaction that leads to a serious life-threatening condition, called Sepsis. Currently, therapeutic protocols through timely antibiotic resuscitation strategie...
Sepsis Child Intensive Care Units, Pediatric Humans Neural Networks, Computer Prognosis Heart Rate Early Diagnosis
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Practical Machine Learning-Based Sepsis Prediction.

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
Sepsis is a life-threatening clinical syndrome and one of the most expensive conditions treated in hospitals. It is challenging to detect due to the nonspecific clinical signs and the absence of gold standard diagnostics. However, early recognition o...
Machine Learning Sepsis Critical Care Humans
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Potential Prognostic Markers in the Heart Rate Variability Features for Early Diagnosis of Sepsis in the Pediatric Intensive Care Unit using Convolutional Neural Network Classifiers.

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
Blood infection due to different circumstances could immediately develop to an extreme body reaction that leads to a serious life-threatening condition, called Sepsis. Currently, therapeutic protocols through timely antibiotic resuscitation strategie...
Heart Rate Early Diagnosis Humans Neural Networks, Computer Prognosis Sepsis Child Intensive Care Units, Pediatric
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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 ...
Sepsis Diagnosis, Differential Organ Dysfunction Scores Intensive Care Units Electronic Health Records Sensitivity and Specificity Humans Machine Learning
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Too Many Definitions of Sepsis: Can Machine Learning Leverage the Electronic Health Record to Increase Accuracy and Bring Consensus?

Critical care medicine Feb 1, 2020
Electronic Health Records Reproducibility of Results Sepsis Health Status Indicators Humans Machine Learning
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Postimplementation Evaluation of a Machine Learning-Based Deterioration Risk Alert to Enhance Sepsis Outcome Improvements.

Nursing administration quarterly Jan 1, 2020
Machine learning-based early warning systems (EWSs) can detect clinical deterioration more accurately than point-score tools. In patients with sepsis, however, the timing and scope of sepsis interventions relative to an advanced EWS alert are not wel...
Machine Learning Female Aged Humans Sepsis Outcome Assessment, Health Care Aged, 80 and over Program Evaluation Male Middle Aged
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Assessing clinical heterogeneity in sepsis through treatment patterns and machine learning.

Journal of the American Medical Informatics Association : JAMIA Dec 1, 2019
OBJECTIVE: To use unsupervised topic modeling to evaluate heterogeneity in sepsis treatment patterns contained within granular data of electronic health records.
Quality of Health Care Hospital Mortality Unsupervised Machine Learning Aged Patient Acuity Aged, 80 and over Adult Electronic Health Records Humans Male Female Anti-Bacterial Agents Middle Aged Hospitalization Retrospective Studies Sepsis
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A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice.

Critical care medicine Nov 1, 2019
OBJECTIVES: Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes.
Cohort Studies Humans Text Messaging Decision Support Systems, Clinical Machine Learning Algorithms Shock, Septic Sensitivity and Specificity Hospitals, Teaching Retrospective Studies Diagnosis, Computer-Assisted Electronic Health Records Sepsis
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Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock.

Critical care medicine Nov 1, 2019
OBJECTIVE: To assess clinician perceptions of a machine learning-based early warning system to predict severe sepsis and septic shock (Early Warning System 2.0).
Machine Learning Practice Patterns, Physicians' Sepsis Algorithms Text Messaging Prospective Studies Humans Electronic Health Records Decision Support Systems, Clinical Medical Staff, Hospital Nursing Staff, Hospital Diagnosis, Computer-Assisted Shock, Septic Practice Patterns, Nurses' Attitude of Health Personnel Hospitals, Teaching
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Prospect of using deep learning for predicting differentiation of myeloid progenitor cells after sepsis.

Chinese medical journal Aug 5, 2019
Myeloid Progenitor Cells Sepsis Humans Cell Differentiation Deep Learning Hematopoiesis
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