AIMC Topic: Emergency Service, Hospital

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Classification of hospital admissions into emergency and elective care: a machine learning approach.

Health care management science Nov 25, 2017
Rising admissions from emergency departments (EDs) to hospitals are a primary concern for many healthcare systems. The issue of how to differentiate urgent admissions from non-urgent or even elective admissions is crucial. We aim to develop a model f...
Hospitalization Aged Aged, 80 and over Young Adult Adult Humans Machine Learning Algorithms Infant, Newborn Age Factors Middle Aged Emergency Medical Services Infant Emergency Service, Hospital Adolescent Elective Surgical Procedures Child, Preschool Patient Admission Child
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A universal deep learning approach for modeling the flow of patients under different severities.

Computer methods and programs in biomedicine Nov 7, 2017
BACKGROUND AND OBJECTIVE: The Accident and Emergency Department (A&ED) is the frontline for providing emergency care in hospitals. Unfortunately, relative A&ED resources have failed to keep up with continuously increasing demand in recent years, whic...
Algorithms Clinical Decision-Making Reproducibility of Results Severity of Illness Index Models, Theoretical Triage Emergency Service, Hospital Hong Kong Stochastic Processes Humans Neural Networks, Computer Machine Learning
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Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments.

Artificial intelligence in medicine Oct 18, 2017
Long length of stay and overcrowding in emergency departments (EDs) are two common problems in the healthcare industry. To decrease the average length of stay (ALOS) and tackle overcrowding, numerous resources, including the number of doctors, nurses...
Humans Neural Networks, Computer Machine Learning Decision Support Techniques Workflow Patient Discharge Efficiency, Organizational Nonlinear Dynamics Decision Support Systems, Management Computer Simulation Length of Stay Time Factors Emergency Service, Hospital Delivery of Health Care, Integrated Hospitals, Teaching Health Services Needs and Demand Patient Admission Patient Care Team Needs Assessment
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Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index.

Annals of emergency medicine Sep 6, 2017
STUDY OBJECTIVE: Standards for emergency department (ED) triage in the United States rely heavily on subjective assessment and are limited in their ability to risk-stratify patients. This study seeks to evaluate an electronic triage system (e-triage)...
Vital Signs United States Triage Area Under Curve Emergency Service, Hospital Humans Machine Learning Algorithms Retrospective Studies Female Adult Cross-Sectional Studies Male
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Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks.

Methods of information in medicine Aug 16, 2017
OBJECTIVE: To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural langua...
Vital Signs Adolescent ROC Curve Models, Theoretical Natural Language Processing Emergency Service, Hospital Young Adult Adult Male Middle Aged Humans Neural Networks, Computer Female Hospitalization Aged
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Early Detection of Peak Demand Days of Chronic Respiratory Diseases Emergency Department Visits Using Artificial Neural Networks.

IEEE journal of biomedical and health informatics Apr 26, 2017
Chronic respiratory diseases, mainly asthma and chronic obstructive pulmonary disease (COPD), affect the lives of people by limiting their activities in various aspects. Overcrowding of hospital emergency departments (EDs) due to respiratory diseases...
Chronic Disease Emergency Service, Hospital Air Pollutants Weather Medical Informatics Humans Neural Networks, Computer Pulmonary Disease, Chronic Obstructive Asthma
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Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning.

PloS one Apr 6, 2017
OBJECTIVE: To demonstrate the incremental benefit of using free text data in addition to vital sign and demographic data to identify patients with suspected infection in the emergency department.
Retrospective Studies Female Aged Adult Male Middle Aged Humans Decision Support Systems, Clinical Machine Learning Automation Triage International Classification of Diseases Emergency Service, Hospital
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Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD.

Translational psychiatry Mar 21, 2017
To date, studies of biological risk factors have revealed inconsistent relationships with subsequent post-traumatic stress disorder (PTSD). The inconsistent signal may reflect the use of data analytic tools that are ill equipped for modeling the comp...
Blood Pressure Humans Female Young Adult Adult Male Area Under Curve Emergency Service, Hospital Norepinephrine Risk Assessment Stress Disorders, Post-Traumatic ROC Curve Support Vector Machine Longitudinal Studies Hydrocortisone Saliva Lymphocytes Heart Rate Adrenocorticotropic Hormone Machine Learning Algorithms Receptors, Glucocorticoid Linear Models Urine Wounds and Injuries Psychological Trauma
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Will they participate? Predicting patients' response to clinical trial invitations in a pediatric emergency department.

Journal of the American Medical Informatics Association : JAMIA Apr 27, 2016
OBJECTIVE: (1) To develop an automated algorithm to predict a patient's response (ie, if the patient agrees or declines) before he/she is approached for a clinical trial invitation; (2) to assess the algorithm performance and the predictors on real-w...
Patient Participation Male Patient Acceptance of Health Care Child Humans Emergency Service, Hospital Machine Learning Hospitals, Pediatric Algorithms Clinical Trials as Topic Female
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Electronic medical record phenotyping using the anchor and learn framework.

Journal of the American Medical Informatics Association : JAMIA Apr 23, 2016
BACKGROUND: Electronic medical records (EMRs) hold a tremendous amount of information about patients that is relevant to determining the optimal approach to patient care. As medicine becomes increasingly precise, a patient's electronic medical record...
Databases as Topic Decision Making, Computer-Assisted Humans Phenotype Machine Learning Electronic Health Records Natural Language Processing Area Under Curve Emergency Service, Hospital
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