AIMC Topic: Unsupervised Machine Learning

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The relationship between clinical subtypes, prognosis, and treatment in ICU patients with acute cholangitis using unsupervised machine learning methods.

BMC infectious diseases Aug 4, 2025
BACKGROUND: Acute cholangitis (AC) presents with significant clinical heterogeneity, and existing severity classifications have limited prognostic value in critically ill patients. Subtypes of AC in critically ill patients have not been investigated.
Humans Intensive Care Units Female Aged Male Prognosis Retrospective Studies Critical Illness Cholangitis Middle Aged Unsupervised Machine Learning Acute Disease
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Unsupervised machine learning approach to interpret complex lower urinary tract symptoms and their impact on quality of life in adult women.

World journal of urology Aug 4, 2025
PURPOSE: To identify clinically meaningful clusters of lower urinary tract symptoms (LUTS) in adult women using an unsupervised machine learning approach and to examine their associations with patient-centered outcomes, including quality of life (QoL...
Unsupervised Machine Learning Female Japan Aged Adult Humans Lower Urinary Tract Symptoms Middle Aged Young Adult Machine Learning Quality of Life
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A hybrid supervised and unsupervised machine learning approach for identifying nucleoside drugs using nanopore readouts.

Nanoscale Jul 31, 2025
Nucleoside drugs, mimics of natural nucleosides, have become cornerstone treatments in clinical approaches to combat cancer and viral infections. The analysis of nucleoside drugs is commonly performed using liquid chromatography-tandem mass spectrome...
Tandem Mass Spectrometry Nanopores Unsupervised Machine Learning Supervised Machine Learning Nucleosides Humans Antiviral Agents Chromatography, Liquid Machine Learning Antineoplastic Agents
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Decoding corporate communication strategies: Analysing mandatory published information under Pillar 3 across turbulent periods with unsupervised machine learning.

PloS one Jul 31, 2025
This study explores the communication patterns of Slovak banks with stakeholders through mandatory disclosures mandated by Basel III's Pillar 3 framework and annual reports in 2007-2022. Our primary objective is to identify key topics communicated by...
Communication Unsupervised Machine Learning Humans COVID-19
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SpaSEG: unsupervised deep learning for multi-task analysis of spatially resolved transcriptomics.

Genome biology Jul 29, 2025
Spatially resolved transcriptomics (SRT) for characterizing spatial cellular heterogeneities in tissue environments requires systematic analytical approaches to elucidate gene expression variations within their physiological context. Here, we introdu...
Carcinoma, Ductal, Breast Female Breast Neoplasms Gene Expression Profiling Transcriptome Deep Learning Unsupervised Machine Learning Humans
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Contrastive learning enhanced pseudo-labeling for unsupervised domain adaptation in person re-identification.

PloS one Jul 14, 2025
Person re-identification (ReID) technology has many applications in intelligent surveillance and public safety. However, the domain difference between the source and target domains makes the generalization ability of the model extremely challenging. ...
Unsupervised Machine Learning Biometric Identification Humans Algorithms
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Unsupervised learning using EHR and census data to identify distinct subphenotypes of newly diagnosed hypertension patients.

PloS one Jul 9, 2025
BACKGROUND: Hypertension (HTN) is a complex condition with significant heterogeneity in presentation and treatment response. Identifying distinct subphenotypes of HTN may improve our understanding of its underlying mechanisms and guide more precise t...
Adult Male Middle Aged Diabetes Mellitus, Type 2 United States Hypertension Electronic Health Records Censuses Female Humans Body Mass Index Aged Phenotype Unsupervised Machine Learning Medicaid
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Identifying and characterising asthma subgroups at high risk of severe exacerbations using machine learning and longitudinal real-world data.

BMJ health & care informatics Jul 7, 2025
OBJECTIVES: To identify and characterise distinct subgroups of patients with asthma with severe acute exacerbations (AEs) by using a multistep clustering methodology that combines supervised and unsupervised machine learning.
Asthma Male Middle Aged Female Unsupervised Machine Learning Aged Humans Adolescent Adult Machine Learning United States Risk Assessment Longitudinal Studies Disease Progression Cluster Analysis Severity of Illness Index
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Unsupervised single-image super-resolution for infant brain MRI.

NeuroImage Jun 21, 2025
Acquiring high-resolution (HR) MR images of infant brains is challenging due to lengthy scan times and limited subject compliance. Image super-resolution (SR) techniques can generate HR images from low-resolution (LR) inputs, reducing the need for ex...
Humans Brain Female Image Processing, Computer-Assisted Magnetic Resonance Imaging Infant, Newborn Unsupervised Machine Learning Male Neuroimaging Infant
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Unsupervised learning reveals rapid gait adaptation after leg loss and regrowth in spiders.

The Journal of experimental biology Jun 17, 2025
Many invertebrates voluntarily lose (autotomize) limbs during antagonistic encounters, and some regenerate functional replacements. Because limb loss can have severe consequences on individual fitness, it is likely subject to significant selective pr...
Locomotion Spiders Biomechanical Phenomena Gait Extremities Animals Regeneration Adaptation, Physiological Unsupervised Machine Learning Video Recording
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