AIMC Topic: Machine Learning

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Using Natural Language Processing to Identify Stigmatizing Language in Labor and Birth Clinical Notes.

Maternal and child health journal
INTRODUCTION: Stigma and bias related to race and other minoritized statuses may underlie disparities in pregnancy and birth outcomes. One emerging method to identify bias is the study of stigmatizing language in the electronic health record. The obj...

Chemprop: A Machine Learning Package for Chemical Property Prediction.

Journal of chemical information and modeling
Deep learning has become a powerful and frequently employed tool for the prediction of molecular properties, thus creating a need for open-source and versatile software solutions that can be operated by nonexperts. Among the current approaches, direc...

Relationship between external and internal load indicators and injury using machine learning in professional soccer: a systematic review and meta-analysis.

Research in sports medicine (Print)
This study verified the relationship between internal load (IL) and external load (EL) and their association on injury risk (IR) prediction considering machine learning (ML) approaches. Studies were included if: (1) participants were male professiona...

Discriminative fusion of moments-aligned latent representation of multimodality medical data.

Physics in medicine and biology
Fusion of multimodal medical data provides multifaceted, disease-relevant information for diagnosis or prognosis prediction modeling. Traditional fusion strategies such as feature concatenation often fail to learn hidden complementary and discriminat...

Development of an explainable artificial intelligence model for Asian vascular wound images.

International wound journal
Chronic wounds contribute to significant healthcare and economic burden worldwide. Wound assessment remains challenging given its complex and dynamic nature. The use of artificial intelligence (AI) and machine learning methods in wound analysis is pr...

Machine learning for layer-by-layer nanofiltration membrane performance prediction and polymer candidate exploration.

Chemosphere
In this study, machine learning-based models were established for layer-by-layer (LBL) nanofiltration (NF) membrane performance prediction and polymer candidate exploration. Four different models, i.e., linear, random forest (RF), boosted tree (BT), ...

Mapping Cell Atlases at the Single-Cell Level.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Recent advancements in single-cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characteri...

Machine Learning to Advance Human Genome-Wide Association Studies.

Genes
Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data are made available. With the help of these methods, we can decipher information from large...

T-MGCL: Molecule Graph Contrastive Learning Based on Transformer for Molecular Property Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
In recent years, machine learning has gained increasing traction in the study of molecules, enabling researchers to tackle challenging tasks including molecular property prediction and drug design.Consequently, there remains an open challenge to deve...