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Hospital Mortality

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Machine Learning and Artificial Intelligence in Pediatric Research: Current State, Future Prospects, and Examples in Perioperative and Critical Care.

The Journal of pediatrics Jun 1, 2020
Risk Assessment Postoperative Complications Monitoring, Intraoperative Ultrasonography Patient Admission Hospital Mortality Intraoperative Complications Humans Heart Arrest Artificial Intelligence Airway Management Machine Learning Intensive Care Units, Pediatric Perioperative Care Algorithms Blood Transfusion Biomedical Research Critical Care Hemorrhage Anesthesia
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Deep learning-based quantitative visualization and measurement of extraperitoneal hematoma volumes in patients with pelvic fractures: Potential role in personalized forecasting and decision support.

The journal of trauma and acute care surgery Mar 1, 2020
INTRODUCTION: Admission computed tomography (CT) is a widely used diagnostic tool for patients with pelvic fractures. In this pilot study, we hypothesized that pelvic hematoma volumes derived using a rapid automated deep learning-based quantitative v...
Humans Algorithms Deep Learning Pilot Projects Hospital Mortality Decision Support Systems, Clinical Pelvic Bones Retrospective Studies Female Adult Male Middle Aged Fractures, Bone Blood Transfusion Hematoma Embolization, Therapeutic Endotamponade
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Temporal convolutional networks allow early prediction of events in critical care.

Journal of the American Medical Informatics Association : JAMIA Mar 1, 2020
OBJECTIVE: Clinical interventions and death in the intensive care unit (ICU) depend on complex patterns in patients' longitudinal data. We aim to anticipate these events earlier and more consistently so that staff can consider preemptive action.
Intensive Care Units Female Humans Hospitalization Aged Neural Networks, Computer Male Aged, 80 and over Machine Learning Middle Aged Computer Simulation Hospital Mortality Prognosis Logistic Models Critical Care
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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 Humans Rwanda Triage Machine Learning Child, Preschool Developing Countries Prospective Studies Child Child Mortality Prognosis Hospital Mortality Sex Factors Female Male Infant Severity of Illness Index Adolescent
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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.
Unsupervised Machine Learning Patient Acuity Quality of Health Care Adult Male Middle Aged Sepsis Hospital Mortality Humans Anti-Bacterial Agents Retrospective Studies Electronic Health Records Female Hospitalization Aged Aged, 80 and over
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Machine Learning Prediction Models for In-Hospital Mortality After Transcatheter Aortic Valve Replacement.

JACC. Cardiovascular interventions Jul 22, 2019
OBJECTIVES: This study sought to develop and compare an array of machine learning methods to predict in-hospital mortality after transcatheter aortic valve replacement (TAVR) in the United States.
Transcatheter Aortic Valve Replacement Humans Machine Learning Decision Support Techniques Databases, Factual Risk Assessment Female Clinical Decision-Making Hospital Mortality Risk Factors United States Aged Treatment Outcome Aged, 80 and over Predictive Value of Tests Male
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Determinants of In-Hospital Mortality After Percutaneous Coronary Intervention: A Machine Learning Approach.

Journal of the American Heart Association Mar 5, 2019
Background The ability to accurately predict the occurrence of in-hospital death after percutaneous coronary intervention is important for clinical decision-making. We sought to utilize the New York Percutaneous Coronary Intervention Reporting System...
Registries Percutaneous Coronary Intervention Coronary Artery Disease Data Mining New York Stroke Volume Middle Aged Hospital Mortality Treatment Outcome Age Factors Databases, Factual Female Risk Factors Aged Male Humans Machine Learning
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Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department.

Medicine Feb 1, 2019
Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving outcomes. We compared the performance of machine learning models against conventional risk stratifi...
Male Singapore Systemic Inflammatory Response Syndrome Middle Aged Machine Learning ROC Curve Heart Rate Humans Severity of Illness Index Female Socioeconomic Factors Organ Dysfunction Scores Sepsis Reproducibility of Results Triage Hospital Mortality Aged Emergency Service, Hospital Aged, 80 and over
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Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality.

Anesthesiology Oct 1, 2018
WHAT WE ALREADY KNOW ABOUT THIS TOPIC: WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: The authors tested the hypothesis that deep neural networks trained on intraoperative features can predict postoperative in-hospital mortality.
Female Humans Aged Postoperative Complications Adult Hospital Mortality Male Neural Networks, Computer ROC Curve Middle Aged Machine Learning Predictive Value of Tests
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Evaluating ICU Clinical Severity Scoring Systems and Machine Learning Applications: APACHE IV/IVa Case Study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
Clinical scoring systems have been developed for many specific applications, yet they remain underutilized for common reasons such as model inaccuracy and difficulty of use. For intensive care units specifically, the Acute Physiology and Chronic Heal...
Hospital Mortality APACHE Humans Machine Learning Intensive Care Units Severity of Illness Index
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