AIMC Topic: Washington

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A machine learning approach to detect potentially harmful and protective suicide-related content in broadcast media.

PloS one May 14, 2024
Suicide-related media content has preventive or harmful effects depending on the specific content. Proactive media screening for suicide prevention is hampered by the scarcity of machine learning approaches to detect specific characteristics in news ...
Humans Machine Learning Deep Learning Suicide Prevention Suicide Mass Media Washington Oregon
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Predicting Outcomes of Antidepressant Treatment in Community Practice Settings.

Psychiatric services (Washington, D.C.) Dec 5, 2023
OBJECTIVE: The authors examined whether machine-learning models could be used to analyze data from electronic health records (EHRs) to predict patients' responses to antidepressant medications.
Depressive Disorder Patient Health Questionnaire Washington Community Mental Health Services Female Humans Machine Learning Aged Male Electronic Health Records Young Adult Adult Middle Aged Outcome Assessment, Health Care Antidepressive Agents
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Using natural language processing to identify child maltreatment in health systems.

Child abuse & neglect Feb 8, 2023
BACKGROUND: Rates of child maltreatment (CM) obtained from electronic health records are much lower than national child welfare prevalence rates indicate. There is a need to understand how CM is documented to improve reporting and surveillance.
Infant, Newborn Infant Adolescent Child, Preschool Humans Electronic Health Records Female Washington Child Natural Language Processing International Classification of Diseases Child Abuse
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Measuring and modelling perceptions of the built environment for epidemiological research using crowd-sourcing and image-based deep learning models.

Journal of exposure science & environmental epidemiology Nov 11, 2022
BACKGROUND: Perceptions of the built environment, such as nature quality, beauty, relaxation, and safety, may be key factors linking the built environment to human health. However, few studies have examined these types of perceptions due to the diffi...
Humans Deep Learning Epidemiologic Studies Washington
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A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization.

JAMA network open Oct 1, 2021
IMPORTANCE: Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic.
Clinical Decision Rules Benchmarking COVID-19 Testing Crowdsourcing Washington Infant Severity of Illness Index Adolescent Child, Preschool Child ROC Curve Models, Statistical Area Under Curve Young Adult Adult Male Middle Aged Infant, Newborn Hospitalization Aged Aged, 80 and over Humans Algorithms Machine Learning Prognosis COVID-19 Female
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Identifying bladder rupture following traumatic pelvic fracture: A machine learning approach.

Injury Dec 9, 2019
INTRODUCTION: Bladder rupture following blunt pelvic trauma is rare though can have significant sequelae. We sought to determine whether machine learning could help predict the presence of bladder injury using certain factors at the time of presentat...
Humans Machine Learning Databases, Factual Retrospective Studies Urinary Bladder Wounds, Nonpenetrating Urinary Bladder Diseases ROC Curve Washington Area Under Curve Logistic Models Radiography Pelvic Bones Bayes Theorem Fractures, Bone Rupture Decision Trees
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Initial report of safety and procedure duration of robotic-assisted chronic total occlusion coronary intervention.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions Sep 4, 2019
BACKGROUND: No previous reports have examined the impact of robotic-assisted (RA) chronic total occlusion (CTO) PCI on procedural duration or safety compared to totally manual CTO PCI.
Washington Missouri Stents Therapy, Computer-Assisted Coronary Occlusion Angioplasty, Balloon, Coronary Risk Assessment Time Factors Treatment Outcome Robotics Risk Factors Humans Aged Patient Safety Male Retrospective Studies Middle Aged Female
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Using natural language processing of clinical text to enhance identification of opioid-related overdoses in electronic health records data.

Pharmacoepidemiology and drug safety Jun 19, 2019
PURPOSE: To enhance automated methods for accurately identifying opioid-related overdoses and classifying types of overdose using electronic health record (EHR) databases.
Self-Injurious Behavior Risk Analgesics, Opioid Heroin Humans Opioid-Related Disorders Electronic Health Records Washington Sensitivity and Specificity Datasets as Topic Predictive Value of Tests Drug Overdose Natural Language Processing
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Identification of significant factors in fatal-injury highway crashes using genetic algorithm and neural network.

Accident; analysis and prevention Dec 22, 2017
Identification of the significant factors of traffic crashes has been a primary concern of the transportation safety research community for many years. A fatal-injury crash is a comprehensive result influenced by multiple variables involved at the mo...
Automobile Driving Risk Factors Humans Seat Belts Male Neural Networks, Computer Washington Age Factors Algorithms Accidents, Traffic Female
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Prediction and interpretation of crash severity using machine learning based on imbalanced traffic crash data.

Journal of safety research Jul 1, 2025
INTRODUCTION: Predicting and interpreting crash severity is essential for developing cost-effective safety measures. Machine learning (ML) models in crash severity studies have attracted much attention recently due to their promising predicted perfor...
Humans Machine Learning Risk Factors Accidents, Traffic Washington
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