AIMC Topic: Hospital Mortality

Clear Filters Showing 361 to 370 of 385 articles

Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data.

Journal of the American Medical Informatics Association : JAMIA Dec 9, 2020
OBJECTIVE: In applying machine learning (ML) to electronic health record (EHR) data, many decisions must be made before any ML is applied; such preprocessing requires substantial effort and can be labor-intensive. As the role of ML in health care gro...
Humans Hospital Mortality ROC Curve Data Management Respiratory Insufficiency Data Mining Shock Machine Learning Databases, Factual Intensive Care Units Electronic Health Records Risk Assessment
View on PubMed DOI

Dr. Agent: Clinical predictive model via mimicked second opinions.

Journal of the American Medical Informatics Association : JAMIA Jul 1, 2020
OBJECTIVE: Prediction of disease phenotypes and their outcomes is a difficult task. In practice, patients routinely seek second opinions from multiple clinical experts for complex disease diagnosis. Our objective is to mimic such a practice of seekin...
Length of Stay Hospital Mortality Logistic Models Humans Referral and Consultation Neural Networks, Computer Computer Simulation Prognosis Disease Progression Deep Learning
View on PubMed DOI

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
Humans Postoperative Complications Blood Transfusion Monitoring, Intraoperative Artificial Intelligence Intraoperative Complications Critical Care Ultrasonography Machine Learning Heart Arrest Hemorrhage Hospital Mortality Algorithms Airway Management Anesthesia Intensive Care Units, Pediatric Perioperative Care Biomedical Research Patient Admission Risk Assessment
View on PubMed DOI

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...
Blood Transfusion Hematoma Embolization, Therapeutic Endotamponade Female Adult Male Middle Aged Deep Learning Pilot Projects Hospital Mortality Decision Support Systems, Clinical Pelvic Bones Fractures, Bone Humans Algorithms Retrospective Studies
View on PubMed DOI

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.
Neural Networks, Computer Machine Learning Computer Simulation Prognosis Intensive Care Units Humans Middle Aged Hospital Mortality Logistic Models Critical Care Female Hospitalization Aged Aged, 80 and over Male
View on PubMed DOI

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...
Humans Machine Learning Prospective Studies Infections Prognosis Vital Signs Female Male Infant Adolescent Age Factors Rwanda Triage Child, Preschool Severity of Illness Index Developing Countries Child Hospital Mortality Child Mortality Sex Factors
View on PubMed DOI

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.
Patient Acuity Quality of Health Care Male Middle Aged Sepsis Humans Hospital Mortality Anti-Bacterial Agents Unsupervised Machine Learning Retrospective Studies Aged Aged, 80 and over Adult Electronic Health Records Female Hospitalization
View on PubMed DOI

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.
Predictive Value of Tests Transcatheter Aortic Valve Replacement Male Risk Assessment Hospital Mortality United States Treatment Outcome Humans Clinical Decision-Making Machine Learning Female Decision Support Techniques Risk Factors Databases, Factual Aged Aged, 80 and over
View on PubMed DOI

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...
Male New York Stroke Volume Humans Age Factors Machine Learning Middle Aged Registries Hospital Mortality Databases, Factual Percutaneous Coronary Intervention Treatment Outcome Female Coronary Artery Disease Risk Factors Data Mining Aged
View on PubMed DOI

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...
Emergency Service, Hospital Hospital Mortality Humans Aged, 80 and over Singapore Male Systemic Inflammatory Response Syndrome Machine Learning Heart Rate Middle Aged ROC Curve Female Organ Dysfunction Scores Severity of Illness Index Socioeconomic Factors Reproducibility of Results Sepsis Triage Aged
View on PubMed DOI
Popular Topics
Recent Journals