AIMC Topic: Algorithms

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Ensemble-learning approach improves fracture prediction using genomic and phenotypic data.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
UNLABELLED: This study presents an innovative ensemble machine learning model integrating genomic and clinical data to enhance the prediction of major osteoporotic fractures in older men. The Super Learner (SL) model achieved superior performance (AU...

Prediction of postpartum depression in women: development and validation of multiple machine learning models.

Journal of translational medicine
BACKGROUND: Postpartum depression (PPD) is a significant public health issue. This study aimed to develop and validate machine learning (ML) models using biopsychosocial predictors to predict the risk of PPD for perinatal women and to provide several...

Physics-informed machine learning for automatic model reduction in chemical reaction networks.

Scientific reports
Physics-informed machine learning bridges the gap between the high fidelity of mechanistic models and the adaptive insights of artificial intelligence. In chemical reaction network modeling, this synergy proves valuable, addressing the high computati...

Early prediction of postpartum dyslipidemia in gestational diabetes using machine learning models.

Scientific reports
This study addresses a gap in research on predictive models for postpartum dyslipidemia in women with gestational diabetes mellitus (GDM). The goal was to develop a machine learning-based model to predict postpartum dyslipidemia using early pregnancy...

Hybrid neural network method for damage localization in structural health monitoring.

Scientific reports
The detection of cracks in large structures is of critical importance, as such damage can result not only in significant financial costs but also pose serious risks to public safety. Many existing methods for crack detection rely on deep learning alg...

Skin lesion segmentation with a multiscale input fusion U-Net incorporating Res2-SE and pyramid dilated convolution.

Scientific reports
Skin lesion segmentation is crucial for identifying and diagnosing skin diseases. Accurate segmentation aids in identifying and localizing diseases, monitoring morphological changes, and extracting features for further diagnosis, especially in the ea...

FIORA: Local neighborhood-based prediction of compound mass spectra from single fragmentation events.

Nature communications
Non-targeted metabolomics holds great promise for advancing precision medicine and biomarker discovery. However, identifying compounds from tandem mass spectra remains a challenging task due to the incomplete nature of spectral reference libraries. A...

Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.

Journal of medical Internet research
BACKGROUND: Tuberculosis (TB) remains a significant health concern, contributing to the highest mortality among infectious diseases worldwide. However, none of the various TB diagnostic tools introduced is deemed sufficient on its own for the diagnos...

An interpretable machine learning-assisted diagnostic model for Kawasaki disease in children.

Scientific reports
Kawasaki disease (KD) is a syndrome of acute systemic vasculitis commonly observed in children. Due to its unclear pathogenesis and the lack of specific diagnostic markers, it is prone to being confused with other diseases that exhibit similar sympto...

Algorithm, expert, or both? Evaluating the role of feature selection methods on user preferences and reliance.

PloS one
The integration of users and experts in machine learning is a widely studied topic in artificial intelligence literature. Similarly, human-computer interaction research extensively explores the factors that influence the acceptance of AI as a decisio...