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South Carolina

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Designing and executing a functional exercise to test a novel informatics tool for mass casualty triage.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The testing of informatics tools designed for use during mass casualty incidents presents a unique problem as there is no readily available population of victims or identical exposure setting. The purpose of this article is to describe the...

An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: In an effort to improve the efficiency of computer algorithms applied to screening for coronavirus disease 2019 (COVID-19) testing, we used natural language processing and artificial intelligence-based methods with unstructured patient dat...

Deep-Learning-Based detection of recreational vessels in an estuarine soundscape in the May River, South Carolina, USA.

PloS one
This paper presents a deep-learning-based method to detect recreational vessels. The method takes advantage of existing underwater acoustic measurements from an Estuarine Soundscape Observatory Network based in the estuaries of South Carolina (SC), U...

Use of machine learning approaches to predict transition of retention in care among people living with HIV in South Carolina: a real-world data study.

AIDS care
Maintaining retention in care (RIC) for people living with HIV (PLWH) helps achieve viral suppression and reduce onward transmission. This study aims to identify the best machine learning model that predicts the RIC transition over time. Extracting f...

The stroke outcome optimization project: Acute ischemic strokes from a comprehensive stroke center.

Scientific data
Stroke is a leading cause of disability, and Magnetic Resonance Imaging (MRI) is routinely acquired for acute stroke management. Publicly sharing these datasets can aid in the development of machine learning algorithms, particularly for lesion identi...

Using Machine Learning Techniques to Predict Viral Suppression Among People With HIV.

Journal of acquired immune deficiency syndromes (1999)
BACKGROUND: This study aims to develop and examine the performance of machine learning (ML) algorithms in predicting viral suppression among statewide people living with HIV (PWH) in South Carolina.