AIMC Topic: ROC Curve
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Predicting early cessation of exclusive breastfeeding using machine learning techniques.
PloS one
Jan 9, 2025
BACKGROUND: Identification of mother-infant pairs predisposed to early cessation of exclusive breastfeeding is important for delivering targeted support. Machine learning techniques enable development of transparent prediction models that enhance cli...
Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques.
PloS one
Jan 9, 2025
Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models to facilitate early detection and intervention. While state of the art work has focused on various ma...
Machine learning-based analyses of contributing factors for the development of hypertension: a comparative study.
Clinical and experimental hypertension (New York, N.Y. : 1993)
Jan 8, 2025
OBJECTIVES: Sufficient attention has not been given to machine learning (ML) models using longitudinal data for investigating important predictors of new onset of hypertension. We investigated the predictive ability of several ML models for the devel...
Artificial Intelligence Models May Aid in Predicting Lymph Node Metastasis in Patients with T1 Colorectal Cancer.
Gut and liver
Jan 8, 2025
BACKGROUND/AIMS: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predict...
Exploring artificial intelligence for differentiating early syphilis from other skin lesions: a pilot study.
BMC infectious diseases
Jan 8, 2025
BACKGROUND: Early diagnosis of syphilis is vital for its effective control. This study aimed to develop an Artificial Intelligence (AI) diagnostic model based on radiomics technology to distinguish early syphilis from other clinical skin lesions.
Public Health Discussions on Social Media: Evaluating Automated Sentiment Analysis Methods.
JMIR formative research
Jan 8, 2025
BACKGROUND: Sentiment analysis is one of the most widely used methods for mining and examining text. Social media researchers need guidance on choosing between manual and automated sentiment analysis methods.
A Plasma Proteomics-Based Model for Identifying the Risk of Postpartum Depression Using Machine Learning.
Journal of proteome research
Jan 7, 2025
Postpartum depression (PPD) poses significant risks to maternal and infant health, yet proteomic analyses of PPD-risk women remain limited. This study analyzed plasma samples from 30 healthy postpartum women and 30 PPD-risk women using mass spectrome...
External validation of an artificial intelligence model using clinical variables, including ICD-10 codes, for predicting in-hospital mortality among trauma patients: a multicenter retrospective cohort study.
Scientific reports
Jan 7, 2025
Artificial intelligence (AI) is being increasingly applied in healthcare to improve patient care and clinical outcomes. We previously developed an AI model using ICD-10 (International Classification of Diseases, Tenth Revision) codes with other clini...
An interpretable machine learning model for predicting in-hospital mortality in ICU patients with ventilator-associated pneumonia.
PloS one
Jan 7, 2025
BACKGROUND: Ventilator-associated pneumonia (VAP) is a common nosocomial infection in ICU, significantly associated with poor outcomes. However, there is currently a lack of reliable and interpretable tools for assessing the risk of in-hospital morta...
Machine learning algorithms to predict depression in older adults in China: a cross-sectional study.
Frontiers in public health
Jan 7, 2025
OBJECTIVE: The 2-fold objective of this research is to investigate machine learning's (ML) predictive value for the incidence of depression among China's older adult population and to determine the noteworthy aspects resulting in depression.
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