AIMC Topic: Patient Admission

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Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission.

Scientific reports Jan 13, 2021
The aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hosp...
Machine Learning Retrospective Studies Female Male Humans Intensive Care Units, Pediatric Models, Biological Child Mortality Infant Mortality Patient Admission Infant Child, Preschool Child Hospital Mortality
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Development, implementation, and prospective validation of a model to predict 60-day end-of-life in hospitalized adults upon admission at three sites.

BMC medical informatics and decision making Sep 7, 2020
BACKGROUND: Automated systems that use machine learning to estimate a patient's risk of death are being developed to influence care. There remains sparse transparent reporting of model generalizability in different subpopulations especially for imple...
Electronic Health Records Female Hospitalization Aged Adult Male Middle Aged Humans Aged, 80 and over Young Adult Machine Learning Prospective Studies Prognosis Adolescent Patient Admission Mortality
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Prediction of admission in pediatric emergency department with deep neural networks and triage textual data.

Neural networks : the official journal of the International Neural Network Society Mar 18, 2020
Emergency department (ED) overcrowding is a global condition that severely worsens attention to patients, increases clinical risks and affects hospital cost management. A correct and early prediction of ED's admission is of high value and a motivatio...
Humans Neural Networks, Computer Forecasting Infant Deep Learning Cohort Studies Child, Preschool Child Databases, Factual Female Hospitalization Male Triage Emergency Service, Hospital Pediatrics Patient Admission
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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...
Health Facilities United States Department of Veterans Affairs Patient Admission Hospital Mortality Logistic Models United States Models, Statistical Humans Neural Networks, Computer
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Combining patient visual timelines with deep learning to predict mortality.

PloS one Jul 31, 2019
BACKGROUND: Deep learning algorithms have achieved human-equivalent performance in image recognition. However, the majority of clinical data within electronic health records is inherently in a non-image format. Therefore, creating visual representati...
Humans Retrospective Studies Intensive Care Units Electronic Health Records Neural Networks, Computer Machine Learning Vital Signs Deep Learning Female Hospital Mortality Hospitalization ROC Curve Male Patient Admission Middle Aged
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Modelling outcomes after paediatric brain injury with admission laboratory values: a machine-learning approach.

Pediatric research Jul 26, 2019
BACKGROUND: Severe traumatic brain injury (TBI) is a leading cause of mortality in children, but the accurate prediction of outcomes at the point of admission remains very challenging. Admission laboratory results are a promising potential source of ...
Patient Admission Male Child Treatment Outcome Brain Injuries Humans Machine Learning Female
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Using machine-learning methods to support health-care professionals in making admission decisions.

The International journal of health planning and management Apr 7, 2019
BACKGROUND: Large tertiary hospitals usually face long waiting lines; patients who want to receive hospitalization need to be screened in advance. The patient admission screening process involves a health-care professional ranking patients by analyzi...
Humans Machine Learning Decision Support Systems, Clinical Logistic Models Patient Admission
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A comparison of logistic regression models with alternative machine learning methods to predict the risk of in-hospital mortality in emergency medical admissions via external validation.

Health informatics journal Nov 29, 2018
We compare the performance of logistic regression with several alternative machine learning methods to estimate the risk of death for patients following an emergency admission to hospital based on the patients' first blood test results and physiologi...
ROC Curve Logistic Models Emergency Service, Hospital Patient Admission Humans Machine Learning Hospitalization Hospital Mortality
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The Sydney Triage to Admission Risk Tool (START2) using machine learning techniques to support disposition decision-making.

Emergency medicine Australasia : EMA Nov 23, 2018
OBJECTIVE: To further develop and refine an Emergency Department (ED) in-patient admission prediction model using machine learning techniques.
Logistic Models Triage Area Under Curve Emergency Service, Hospital Humans Bayes Theorem Machine Learning Patient Admission Male Forecasting New South Wales Middle Aged Retrospective Studies Adolescent ROC Curve Female Aged Aged, 80 and over Adult
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Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records.

PLoS medicine Nov 20, 2018
BACKGROUND: Emergency admissions are a major source of healthcare spending. We aimed to derive, validate, and compare conventional and machine learning models for prediction of the first emergency admission. Machine learning methods are capable of ca...
Adolescent Time Factors Socioeconomic Factors Health Status Data Mining Sex Factors Health Services Needs and Demand Age Factors Emergency Service, Hospital Male Middle Aged Risk Assessment England Aged Aged, 80 and over Young Adult Adult Patient Admission Needs Assessment Machine Learning Electronic Health Records Female Reproducibility of Results Risk Factors Humans
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