AIMC Topic: Hospital Mortality

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Comparison of Prediction Model Performance Updating Protocols: Using a Data-Driven Testing Procedure to Guide Updating.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
In evolving clinical environments, the accuracy of prediction models deteriorates over time. Guidance on the design of model updating policies is limited, and there is limited exploration of the impact of different policies on future model performanc...
United States Models, Statistical Health Facilities United States Department of Veterans Affairs Humans Patient Admission Neural Networks, Computer Hospital Mortality Logistic Models
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Using a machine learning approach to predict mortality in critically ill influenza patients: a cross-sectional retrospective multicentre study in Taiwan.

BMJ open Feb 25, 2020
OBJECTIVES: Current mortality prediction models used in the intensive care unit (ICU) have a limited role for specific diseases such as influenza, and we aimed to establish an explainable machine learning (ML) model for predicting mortality in critic...
Adult Cross-Sectional Studies Male Middle Aged Hospital Mortality Humans Machine Learning Models, Theoretical Retrospective Studies Taiwan Critical Illness Influenza, Human Female Outcome Assessment, Health Care
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Prehospital triage of acute aortic syndrome using a machine learning algorithm.

The British journal of surgery Feb 11, 2020
BACKGROUND: Acute aortic syndrome (AAS) comprises a complex and potentially fatal group of conditions requiring emergency specialist management. The aim of this study was to build a prediction algorithm to assist prehospital triage of AAS.
Humans Machine Learning Algorithms Prospective Studies Middle Aged Hospital Mortality Logistic Models Emergency Medical Services Triage Prognosis Female Reproducibility of Results Aged Male Acute Disease Syndrome Aortic Diseases Clinical Decision Rules
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Prospective and External Evaluation of a Machine Learning Model to Predict In-Hospital Mortality of Adults at Time of Admission.

JAMA network open Feb 5, 2020
IMPORTANCE: The ability to accurately predict in-hospital mortality for patients at the time of admission could improve clinical and operational decision-making and outcomes. Few of the machine learning models that have been developed to predict in-h...
Electronic Health Records Female Hospitalization Aged Aged, 80 and over Machine Learning Forecasting Prospective Studies Prognosis Retrospective Studies Hospital Mortality ROC Curve Area Under Curve Models, Biological Hospitals, Teaching Adult Male Middle Aged Humans Risk Assessment Hospitals
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Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction.

Health informatics journal Dec 30, 2019
In order to evaluate mortality predictions based on boosted trees, this retrospective study uses electronic medical record data from three academic health centers for inpatients 18 years or older with at least one observation of each vital sign. Pred...
Hospital Mortality Machine Learning Algorithms Retrospective Studies Sepsis Humans
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A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a Nine-Point Triage Score.

Journal of general internal medicine Nov 1, 2019
BACKGROUND: Emergency departments (ED) are becoming increasingly overwhelmed, increasing poor outcomes. Triage scores aim to optimize the waiting time and prioritize the resource usage. Artificial intelligence (AI) algorithms offer advantages for cre...
Aged Aged, 80 and over Young Adult Adult Middle Aged Adolescent Hospital Mortality Triage Emergency Service, Hospital Humans Artificial Intelligence Machine Learning Retrospective Studies
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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.
Humans Machine Learning Software Algorithms Prognosis Emergency Service, Hospital Adult Shock, Septic Electronic Health Records Multivariate Analysis Support Vector Machine Male Republic of Korea Vital Signs Triage Middle Aged Female Area Under Curve Sepsis Aged Hospital Mortality Young Adult
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An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data.

British journal of anaesthesia Oct 15, 2019
BACKGROUND: Rapid, preoperative identification of patients with the highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores ...
Electronic Health Records Female Risk Factors Aged Aged, 80 and over Young Adult Adult Male Middle Aged Risk Assessment Health Status Humans Postoperative Complications California Machine Learning Comorbidity Preoperative Period Adolescent Hospital Mortality
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ICU staffing feature phenotypes and their relationship with patients' outcomes: an unsupervised machine learning analysis.

Intensive care medicine Oct 8, 2019
PURPOSE: To study whether ICU staffing features are associated with improved hospital mortality, ICU length of stay (LOS) and duration of mechanical ventilation (MV) using cluster analysis directed by machine learning.
Hospital Bed Capacity Odds Ratio Organ Dysfunction Scores Personnel Staffing and Scheduling Physical Therapists Retrospective Studies Time Factors Intensive Care Units Hospital Mortality Humans Brazil Logistic Models Physicians Length of Stay Cluster Analysis Nurses Unsupervised Machine Learning
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Machine Learning to Predict In-Hospital Morbidity and Mortality after Traumatic Brain Injury.

Journal of neurotrauma Sep 18, 2019
Recently, successful predictions using machine learning (ML) algorithms have been reported in various fields. However, in traumatic brain injury (TBI) cohorts, few studies have examined modern ML algorithms. To develop a simple ML model for TBI outco...
Adolescent Humans Child Hospital Mortality Machine Learning Aged, 80 and over Brain Injuries, Traumatic Prognosis Young Adult Female Adult Aged Male Middle Aged
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