AIMC Topic: Cross-Sectional Studies
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Identification of diabetic retinopathy classification using machine learning algorithms on clinical data and optical coherence tomography angiography.
Eye (London, England)
Jun 13, 2024
BACKGROUND: To apply machine learning (ML) algorithms to perform multiclass diabetic retinopathy (DR) classification using both clinical data and optical coherence tomography angiography (OCTA).
Variability of Guidelines and Disclosures for AI-Generated Content in Top Surgical Journals.
Surgical innovation
Jun 11, 2024
: When properly utilized, artificial intelligence generated content (AIGC) may improve virtually every aspect of research, from data gathering to synthesis. Nevertheless, when used inappropriately, the use of AIGC may lead to the dissemination of ina...
Equity in Using Artificial Intelligence Mortality Predictions to Target Goals of Care Documentation.
Journal of general internal medicine
Jun 10, 2024
BACKGROUND: Artificial intelligence (AI) algorithms are increasingly used to target patients with elevated mortality risk scores for goals-of-care (GOC) conversations.
High-resolution mapping of essential maternal and child health service coverage in Nigeria: a machine learning approach.
BMJ open
Jun 10, 2024
BACKGROUND: National-level coverage estimates of maternal and child health (MCH) services mask district-level and community-level geographical inequities. The purpose of this study is to estimate grid-level coverage of essential MCH services in Niger...
Using machine learning to unveil relevant predictors of adherence to recommended health-protective behaviors during the COVID-19 pandemic in Denmark.
Applied psychology. Health and well-being
Jun 8, 2024
What were relevant predictors of individuals' proclivity to adhere to recommended health-protective behaviors during the COVID-19 pandemic in Denmark? Applying machine learning (namely, lasso regression) to a repeated cross-sectional survey spanning ...
Interpretable machine learning predicts postpartum hemorrhage with severe maternal morbidity in a lower-risk laboring obstetric population.
American journal of obstetrics & gynecology MFM
Jun 6, 2024
BACKGROUND: Early identification of patients at increased risk for postpartum hemorrhage (PPH) associated with severe maternal morbidity (SMM) is critical for preparation and preventative intervention. However, prediction is challenging in patients w...
Opioid Nonadherence Risk Prediction of Patients with Cancer-Related Pain Based on Five Machine Learning Algorithms.
Pain research & management
Jun 6, 2024
OBJECTIVES: Opioid nonadherence represents a significant barrier to cancer pain treatment efficacy. However, there is currently no effective prediction method for opioid adherence in patients with cancer pain. We aimed to develop and validate a machi...
Two-part predictive modeling for COVID-19 cases and deaths in the U.S.
PloS one
Jun 6, 2024
COVID-19 prediction has been essential in the aid of prevention and control of the disease. The motivation of this case study is to develop predictive models for COVID-19 cases and deaths based on a cross-sectional data set with a total of 28,955 obs...
Identifying the risk of depression in a large sample of adolescents: An artificial neural network based on random forest.
Journal of adolescence
Jun 4, 2024
BACKGROUND: This study aims to develop an artificial neural network (ANN) prediction model incorporating random forest (RF) screening ability for predicting the risk of depression in adolescents and identifies key risk factors to provide a new approa...
Machine learning identifies different related factors associated with depression and suicidal ideation in Chinese children and adolescents.
Journal of affective disorders
Jun 4, 2024
BACKGROUND: Depression and suicidal ideation often co-occur in children and adolescents, yet they possess distinct characteristics. This study sought to identify the different related factors associated with depression and suicidal ideation.
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