AIMC Topic: Emergency Service, Hospital
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Estimation of age in unidentified patients via chest radiography using convolutional neural network regression.
Emergency radiology
Apr 28, 2020
PURPOSE: Patient age has important clinical utility for refining a differential diagnosis in radiology. Here, we evaluate the potential for convolutional neural network models to predict patient age based on anterior-posterior chest radiographs for i...
Comparison of deep learning models for natural language processing-based classification of non-English head CT reports.
Neuroradiology
Apr 25, 2020
PURPOSE: Natural language processing (NLP) can be used for automatic flagging of radiology reports. We assessed deep learning models for classifying non-English head CT reports.
Emergency department disposition prediction using a deep neural network with integrated clinical narratives and structured data.
International journal of medical informatics
Apr 23, 2020
BACKGROUND: Emergency department (ED) overcrowding has been a serious issue and demands effective clinical decision-making of patient disposition. In previous studies, emergency clinical narratives provide a rich context for clinical decisions. We ai...
Exploring the Role of Artificial Intelligence in an Emergency and Trauma Radiology Department.
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Apr 20, 2020
Emergency and trauma radiologists, emergency department's physicians and nurses, researchers, departmental leaders, and health policymakers have attempted to discover efficient approaches to enhance the provision of quality patient care. There are in...
An Integrated Approach of Machine Learning and Systems Thinking for Waiting Time Prediction in an Emergency Department.
International journal of medical informatics
Apr 12, 2020
OBJECTIVE: The objective of this study is to apply machine learning algorithms for real-time and personalized waiting time prediction in emergency departments. We also aim to introduce the concept of systems thinking to enhance the performance of the...
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...
Early short-term prediction of emergency department length of stay using natural language processing for low-acuity outpatients.
The American journal of emergency medicine
Mar 10, 2020
BACKGROUND: Low-acuity outpatients constitute the majority of emergency department (ED) patients, and these patients often experience an unpredictable length of stay (LOS). Effective LOS prediction might improve the quality of ED care and reduce ED c...
Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services.
Scandinavian journal of trauma, resuscitation and emergency medicine
Mar 4, 2020
BACKGROUND: In emergency medical services (EMSs), accurately predicting the severity of a patient's medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an...
HarborBot: A Chatbot for Social Needs Screening.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Mar 4, 2020
Accessing patients' social needs is a critical challenge at emergency departments (EDs). However, most EDs do not have extra staff to administer screeners, and without personnel administration, response rates are low especially for low health literac...
Using machine learning to classify suicide attempt history among youth in medical care settings.
Journal of affective disorders
Feb 28, 2020
BACKGROUND: The current study aimed to classify recent and lifetime suicide attempt history among youth presenting to medical settings using machine learning (ML) as applied to a behavioral health screen self-report survey.
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