AIMC Topic: 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...
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 ...
Humans Machine Learning Sepsis Diagnosis, Differential Intensive Care Units Organ Dysfunction Scores Electronic Health Records Sensitivity and Specificity
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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
Humans Machine Learning Health Status Indicators Electronic Health Records Reproducibility of Results Sepsis
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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 Program Evaluation Aged, 80 and over 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 Patient Acuity Aged Aged, 80 and over Adult Male Middle Aged Sepsis Electronic Health Records Female Hospitalization Anti-Bacterial Agents Retrospective Studies Humans
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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.
Hospitals, Teaching Decision Support Systems, Clinical Shock, Septic Text Messaging Sepsis Humans Cohort Studies Electronic Health Records Sensitivity and Specificity Machine Learning Diagnosis, Computer-Assisted Algorithms Retrospective Studies
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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).
Humans Machine Learning Algorithms Prospective Studies Electronic Health Records Text Messaging Nursing Staff, Hospital Medical Staff, Hospital Practice Patterns, Nurses' Hospitals, Teaching Diagnosis, Computer-Assisted Decision Support Systems, Clinical Attitude of Health Personnel Shock, Septic Practice Patterns, Physicians' Sepsis
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Prospect of using deep learning for predicting differentiation of myeloid progenitor cells after sepsis.

Chinese medical journal Aug 5, 2019
Hematopoiesis Humans Deep Learning Myeloid Progenitor Cells Sepsis Cell Differentiation
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New Phenotypes for Sepsis: The Promise and Problem of Applying Machine Learning and Artificial Intelligence in Clinical Research.

JAMA May 28, 2019
Phenotype Humans Sepsis Artificial Intelligence Machine Learning
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Artificial Intelligence: Progress Towards an Intelligent Clinical Support System.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies Apr 1, 2019
Sepsis Humans Artificial Intelligence Child Intensive Care Units, Pediatric
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