AIMC Topic:
Supervised Machine Learning

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CARE-SD: classifier-based analysis for recognizing provider stigmatizing and doubt marker labels in electronic health records: model development and validation.

Journal of the American Medical Informatics Association : JAMIA Feb 1, 2025
OBJECTIVE: To detect and classify features of stigmatizing and biased language in intensive care electronic health records (EHRs) using natural language processing techniques.
Humans Electronic Health Records Stereotyping Natural Language Processing Supervised Machine Learning Social Stigma
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Supervised machine learning on electrocardiography features to classify sleep in noncritically ill children.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine Feb 1, 2025
STUDY OBJECTIVES: Despite frequent sleep disruption in the pediatric intensive care unit, bedside sleep monitoring in real time is currently not available. Supervised machine learning applied to electrocardiography data may provide a solution, becaus...
Humans Machine Learning Child Electrocardiography Supervised Machine Learning Sleep Polysomnography Infant Adolescent Retrospective Studies Child, Preschool Female Male
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Using Supervised Machine Learning Algorithms to Predict Bovine Leukemia Virus Seropositivity in Florida Beef Cattle: A 10-Year Retrospective Study.

Journal of veterinary internal medicine Jan 1, 2025
BACKGROUND: Bovine leukemia virus (BLV) infection in beef cattle has received less attention than in dairy herds, despite its potential impact on the beef industry.
Animals Male Cattle Retrospective Studies Female Risk Factors Florida Enzootic Bovine Leukosis Leukemia Virus, Bovine Antibodies, Viral Supervised Machine Learning Seroepidemiologic Studies Algorithms
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Unsupervised Dimensionality Reduction Techniques for the Assessment of ASD Biomarkers.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2025
Autism Spectrum Disorder (ASD) encompasses a range of developmental disabilities marked by differences in social functioning, cognition, and behavior. Both genetic and environmental factors are known to contribute to ASD, yet the exact etiological fa...
Neuroimaging Unsupervised Machine Learning Principal Component Analysis Supervised Machine Learning Biomarkers Autism Spectrum Disorder Female Male Child Computational Biology Humans Algorithms
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A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI.

Mathematical biosciences and engineering : MBE Dec 11, 2024
The hippocampus is a small, yet intricate seahorse-shaped tiny structure located deep within the brain's medial temporal lobe. It is a crucial component of the limbic system, which is responsible for regulating emotions, memory, and spatial navigatio...
Algorithms Brain Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Fuzzy Logic Humans Neural Networks, Computer Supervised Machine Learning Hippocampus False Positive Reactions
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Tractography-Based Automated Identification of Retinogeniculate Visual Pathway With Novel Microstructure-Informed Supervised Contrastive Learning.

Human brain mapping Dec 1, 2024
The retinogeniculate visual pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and c...
Visual Pathways Diffusion Tensor Imaging Geniculate Bodies Humans Male Female Deep Learning Adult Image Processing, Computer-Assisted Pituitary Neoplasms Retina Supervised Machine Learning
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Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development.

Clinical and translational science Nov 1, 2024
Despite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML) models for drug development, effectively interpreting their predictions remains a challenge, which limits their impact on clinical decisions. We address this ...
Drug Development Supervised Machine Learning Humans Software
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Utilizing active learning strategies in machine-assisted annotation for clinical named entity recognition: a comprehensive analysis considering annotation costs and target effectiveness.

Journal of the American Medical Informatics Association : JAMIA Nov 1, 2024
OBJECTIVES: Active learning (AL) has rarely integrated diversity-based and uncertainty-based strategies into a dynamic sampling framework for clinical named entity recognition (NER). Machine-assisted annotation is becoming popular for creating gold-s...
Datasets as Topic Electronic Health Records Natural Language Processing Humans Supervised Machine Learning
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Robust self-supervised learning strategy to tackle the inherent sparsity in single-cell RNA-seq data.

Briefings in bioinformatics Sep 23, 2024
Single-cell RNA sequencing (scRNA-seq) is a powerful tool for elucidating cellular heterogeneity and tissue function in various biological contexts. However, the sparsity in scRNA-seq data limits the accuracy of cell type annotation and transcriptomi...
Single-Cell Gene Expression Analysis Gene Expression Profiling Supervised Machine Learning Humans Single-Cell Analysis Computational Biology Algorithms RNA-Seq Sequence Analysis, RNA
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Semi-supervised learning with pseudo-labeling compares favorably with large language models for regulatory sequence prediction.

Briefings in bioinformatics Sep 23, 2024
Predicting molecular processes using deep learning is a promising approach to provide biological insights for non-coding single nucleotide polymorphisms identified in genome-wide association studies. However, most deep learning methods rely on superv...
Humans Algorithms Computational Biology Regulatory Sequences, Nucleic Acid Deep Learning Supervised Machine Learning Genome-Wide Association Study Genome, Human Polymorphism, Single Nucleotide
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