AIMC Topic:
Supervised Machine Learning
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EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation.
Schizophrenia bulletin
May 8, 2025
BACKGROUND: Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity with high temporal resolution. Combined with state-of-the-art machine learning (ML) techniques, EEG rec...
Development and validation of a multi-stage self-supervised learning model for optical coherence tomography image classification.
Journal of the American Medical Informatics Association : JAMIA
May 1, 2025
OBJECTIVE: This study aimed to develop a novel multi-stage self-supervised learning model tailored for the accurate classification of optical coherence tomography (OCT) images in ophthalmology reducing reliance on costly labeled datasets while mainta...
WMH-DualTasker: A Weakly Supervised Deep Learning Model for Automated White Matter Hyperintensities Segmentation and Visual Rating Prediction.
Human brain mapping
Apr 15, 2025
White matter hyperintensities (WMH) are neuroimaging markers linked to an elevated risk of cognitive decline. WMH severity is typically assessed via visual rating scales and through volumetric segmentation. While visual rating scales are commonly use...
Long-term care plan recommendation for older adults with disabilities: a bipartite graph transformer and self-supervised approach.
Journal of the American Medical Informatics Association : JAMIA
Apr 1, 2025
BACKGROUND: With the global population aging and advancements in the medical system, long-term care in healthcare institutions and home settings has become essential for older adults with disabilities. However, the diverse and scattered care requirem...
A semi-supervised learning approach to classify drug attributes in a pharmacy management database: A STROBE-compliant study.
Medicine
Mar 7, 2025
With the development of information and communication technology, it has become possible to improve pharmacy management system (PMS) using these technologies. Our study aims to enhance the accuracy of drug attribute classification and recommend appro...
Optimizing sample size for supervised machine learning with bulk transcriptomic sequencing: a learning curve approach.
Briefings in bioinformatics
Mar 4, 2025
Accurate sample classification using transcriptomics data is crucial for advancing personalized medicine. Achieving this goal necessitates determining a suitable sample size that ensures adequate classification accuracy without undue resource allocat...
Using gut microbiome metagenomic hypervariable features for diabetes screening and typing through supervised machine learning.
Microbial genomics
Mar 1, 2025
Diabetes mellitus is a complex metabolic disorder and one of the fastest-growing global public health concerns. The gut microbiota is implicated in the pathophysiology of various diseases, including diabetes. This study utilized 16S rRNA metagenomic ...
Semi-supervised learning from small annotated data and large unlabeled data for fine-grained Participants, Intervention, Comparison, and Outcomes entity recognition.
Journal of the American Medical Informatics Association : JAMIA
Mar 1, 2025
OBJECTIVE: Extracting PICO elements-Participants, Intervention, Comparison, and Outcomes-from clinical trial literature is essential for clinical evidence retrieval, appraisal, and synthesis. Existing approaches do not distinguish the attributes of P...
SSL-VQ: vector-quantized variational autoencoders for semi-supervised prediction of therapeutic targets across diverse diseases.
Bioinformatics (Oxford, England)
Feb 4, 2025
MOTIVATION: Identifying effective therapeutic targets poses a challenge in drug discovery, especially for uncharacterized diseases without known therapeutic targets (e.g. rare diseases, intractable diseases).
Analysis of Two Neuroanatomical Subtypes of Parkinson's Disease and Their Motor Progression Based on Semi-Supervised Machine Learning.
CNS neuroscience & therapeutics
Feb 1, 2025
BACKGROUND: The high heterogeneity of Parkinson's disease (PD) hinders personalized interventions. Brain structure reflects damage and neuroplasticity and is one of the biological bases of symptomatology. Subtyping PD in the framework of brain struct...
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