AIMC Topic:
Supervised Machine Learning

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[Constructing a predictive model for the death risk of patients with septic shock based on supervised machine learning algorithms].

Zhonghua wei zhong bing ji jiu yi xue Apr 1, 2024
OBJECTIVE: To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms.
Machine Learning Algorithms Prognosis Humans Male Shock, Septic Middle Aged Support Vector Machine Supervised Machine Learning Intensive Care Units Female Decision Trees
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Improving the performance of supervised deep learning for regulatory genomics using phylogenetic augmentation.

Bioinformatics (Oxford, England) Mar 29, 2024
MOTIVATION: Supervised deep learning is used to model the complex relationship between genomic sequence and regulatory function. Understanding how these models make predictions can provide biological insight into regulatory functions. Given the compl...
Humans Genomics Supervised Machine Learning Phylogeny Deep Learning
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Self-Supervised Learning for Improved Optical Coherence Tomography Detection of Macular Telangiectasia Type 2.

JAMA ophthalmology Mar 1, 2024
IMPORTANCE: Deep learning image analysis often depends on large, labeled datasets, which are difficult to obtain for rare diseases.
Retinal Telangiectasis Supervised Machine Learning Rare Diseases Deep Learning Tomography, Optical Coherence Humans Retrospective Studies Male Female Middle Aged
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An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine Mar 1, 2024
STUDY OBJECTIVES: Although sleep is frequently disrupted in the pediatric intensive care unit, it is currently not possible to perform real-time sleep monitoring at the bedside. In this study, spectral band powers of electroencephalography data are u...
Supervised Machine Learning Sleep Child Electroencephalography Polysomnography Humans Retrospective Studies Infant
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scSemiGCN: boosting cell-type annotation from noise-resistant graph neural networks with extremely limited supervision.

Bioinformatics (Oxford, England) Feb 1, 2024
MOTIVATION: Cell-type annotation is fundamental in revealing cell heterogeneity for single-cell data analysis. Although a host of works have been developed, the low signal-to-noise-ratio single-cell RNA-sequencing data that suffers from batch effects...
Neural Networks, Computer Signal-To-Noise Ratio Supervised Machine Learning Single-Cell Analysis
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Semisupervised transfer learning for evaluation of model classification performance.

Biometrics Jan 29, 2024
In many modern machine learning applications, changes in covariate distributions and difficulty in acquiring outcome information have posed challenges to robust model training and evaluation. Numerous transfer learning methods have been developed to ...
Bias Supervised Machine Learning Research Design ROC Curve Humans Machine Learning
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DOME Registry: implementing community-wide recommendations for reporting supervised machine learning in biology.

GigaScience Jan 2, 2024
Supervised machine learning (ML) is used extensively in biology and deserves closer scrutiny. The Data Optimization Model Evaluation (DOME) recommendations aim to enhance the validation and reproducibility of ML research by establishing standards for...
Supervised Machine Learning Reproducibility of Results Humans Registries Databases, Factual
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SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images.

Current medical imaging Jan 1, 2024
INTRODUCTION: This study introduces SkinLiTE, a lightweight supervised contrastive learning model tailored to enhance the detection and typification of skin lesions in dermoscopic images. The core of SkinLiTE lies in its unique integration of supervi...
Image Interpretation, Computer-Assisted Humans Skin Neoplasms Skin Supervised Machine Learning Dermoscopy
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A comparative study of supervised and unsupervised machine learning algorithms applied to human microbiome.

La Clinica terapeutica Jan 1, 2024
BACKGROUND: The human microbiome, consisting of diverse bacte-rial, fungal, protozoan and viral species, exerts a profound influence on various physiological processes and disease susceptibility. However, the complexity of microbiome data has present...
Feces Humans Gastrointestinal Microbiome Adenoma Algorithms RNA, Ribosomal, 16S Supervised Machine Learning Colorectal Neoplasms Unsupervised Machine Learning Microbiota
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Comparative analysis of supervised learning algorithms for prediction of cardiovascular diseases.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2024
BACKGROUND: With the advent of artificial intelligence technology, machine learning algorithms have been widely used in the area of disease prediction.
Humans Supervised Machine Learning Algorithms Cardiovascular Diseases
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