AIMC Topic: Unsupervised Machine Learning

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Interpretable unsupervised learning enables accurate clustering with high-throughput imaging flow cytometry.

Scientific reports Nov 23, 2023
A primary challenge of high-throughput imaging flow cytometry (IFC) is to analyze the vast amount of imaging data, especially in applications where ground truth labels are unavailable or hard to obtain. We present an unsupervised deep embedding algor...
Unsupervised Machine Learning Flow Cytometry Algorithms Cluster Analysis Humans Neural Networks, Computer
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Unsupervised Local Discrimination for Medical Images.

IEEE transactions on pattern analysis and machine intelligence Nov 3, 2023
Contrastive learning, which aims to capture general representation from unlabeled images to initialize the medical analysis models, has been proven effective in alleviating the high demand for expensive annotations. Current methods mainly focus on in...
Cluster Analysis Unsupervised Machine Learning Algorithms Image Processing, Computer-Assisted
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Workflow for Evaluating Normalization Tools for Omics Data Using Supervised and Unsupervised Machine Learning.

Journal of the American Society for Mass Spectrometry Oct 28, 2023
To achieve high quality omics results, systematic variability in mass spectrometry (MS) data must be adequately addressed. Effective data normalization is essential for minimizing this variability. The abundance of approaches and the data-dependent n...
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Workflow Humans Neoplasms Software Unsupervised Machine Learning
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An unsupervised learning approach to identify immunoglobulin utilization patterns using electronic health records.

Transfusion Oct 20, 2023
BACKGROUND: Managing Canada's immunoglobulin (Ig) product resource allocation is challenging due to increasing demand, high expenditure, and global shortages. Detection of groups with high utilization rates can help with resource planning for Ig prod...
Child, Preschool Inpatients Unsupervised Machine Learning Immunoglobulins Electronic Health Records Adult Humans
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Response to Folweiler KA et al., Unsupervised Machine Learning Reveals Novel Traumatic Brain Injury Patient Phenotypes With Distinct Acute Injury Profiles and Long-Term Outcomes (DOI: 10.1089/neu.2019.6705).

Journal of neurotrauma Oct 18, 2023
Brain Injuries, Traumatic Unsupervised Machine Learning Humans Phenotype
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Unsupervised learning for medical data: A review of probabilistic factorization methods.

Statistics in medicine Oct 18, 2023
We review popular unsupervised learning methods for the analysis of high-dimensional data encountered in, for example, genomics, medical imaging, cohort studies, and biobanks. We show that four commonly used methods, principal component analysis, K-m...
Unsupervised Machine Learning Algorithms Genomics Cluster Analysis Models, Statistical Humans
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Segment-Based Unsupervised Learning Method in Sensor-Based Human Activity Recognition.

Sensors (Basel, Switzerland) Oct 13, 2023
Sensor-based human activity recognition (HAR) is a task to recognize human activities, and HAR has an important role in analyzing human behavior such as in the healthcare field. HAR is typically implemented using traditional machine learning methods....
Pregnancy Female Machine Learning Human Activities Recognition, Psychology Unsupervised Machine Learning Labor, Obstetric Humans
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Unsupervised machine learning method for indirect estimation of reference intervals for chronic kidney disease in the Puerto Rican population.

Scientific reports Oct 11, 2023
Reference intervals (RIs) for clinical laboratory values are extremely important for diagnostics and treatment of patients. However, the determination of these ranges is costly and time-consuming. As a result, often different unverified RIs are used ...
Renal Insufficiency, Chronic Unsupervised Machine Learning Humans Puerto Rico Hispanic or Latino Reference Values
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Unsupervised machine learning for species delimitation, integrative taxonomy, and biodiversity conservation.

Molecular phylogenetics and evolution Oct 5, 2023
Integrative taxonomy, combining data from multiple axes of biologically relevant variation, is a major goal of systematics. Ideally, such taxonomies will derive from similarly integrative species-delimitation analyses. Yet, most current methods rely ...
Humans Biodiversity Species Specificity Phylogeny Unsupervised Machine Learning Phenotype
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Procedure code overutilization detection from healthcare claims using unsupervised deep learning methods.

BMC medical informatics and decision making Sep 28, 2023
BACKGROUND: Fraud, Waste, and Abuse (FWA) in medical claims have a negative impact on the quality and cost of healthcare. A major component of FWA in claims is procedure code overutilization, where one or more prescribed procedures may not be relevan...
Delivery of Health Care Fraud Deep Learning Humans Databases, Factual Unsupervised Machine Learning
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