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| http://dx.doi.org/10.1016/j.jaip.2014.12.011 | DOI Listing |
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Development and Evaluation of an Automated Multimodal Mobile Detection of Oral Cancer (mDOC) Imaging System to Aid in Risk-based Management of Oral Mucosal Lesions.
Cancer Prev Res (Phila)
January 2025
Rice University, Houston, Texas, United States.
Oral cancer is a major global health problem. It is commonly diagnosed at an advanced stage although often preceded by clinically visible oral mucosal lesions, termed oral potentially malignant disorders associated with an increased risk for oral cancer development. There is an unmet clinical need for effective screening tools to assist front-line healthcare providers to determine which patients should be referred to an oral cancer specialist for evaluation.
View Article and Find Full Text PDFAssociation of Mobile Stroke Unit Care and Spending, Utilization, and Death in New York City.
J Am Heart Assoc
January 2025
Department of Population Health Sciences Weill Cornell Medicine New York NY.
Background: Transport by mobile stroke units (MSUs), which provide access to computed tomography scanning and intravenous blood pressure medications and thrombolytics, reduces time to treatment and may improve short-term functional outcomes for patients with acute stroke. The longer-term clinical and financial impacts remain incompletely understood. The aim of the study was to determine whether MSU care is associated with better health, utilization, and spending outcomes for patients with suspected acute stroke.
View Article and Find Full Text PDFDefining Research for PhD and DNP Prepared Nurses: A Collaborative Approach.
Res Nurs Health
January 2025
College of Nursing, Florida Atlantic University, Boca Raton, Florida, USA.
Background: The use of digital health strategies for cancer care increased dramatically in the United States over the past 4 years. However, a dearth of knowledge remains about the use of digital health for cancer prevention for some populations with heath disparities. Therefore, the purpose of the present scoping review was to identify digital health interventions for cancer prevention designed for people with disabilities.
View Article and Find Full Text PDFIntroductionAsthma attacks are set off by triggers such as pollutants from the environment, respiratory viruses, physical activity and allergens. The aim of this research is to create a machine learning model using data from mobile health technology to predict and appropriately warn a patient to avoid such triggers.MethodsLightweight machine learning models, XGBoost, Random Forest, and LightGBM were trained and tested on cleaned asthma data with a 70-30 train-test split.
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