AIMC Topic: Unsupervised Machine Learning

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Unsupervised Machine Learning of Topics Documented by Nurses about Hospitalized Patients Prior to a Rapid-Response Event.

Applied clinical informatics Dec 18, 2019
BACKGROUND: In the hospital setting, it is crucial to identify patients at risk for deterioration before it fully develops, so providers can respond rapidly to reverse the deterioration. Rapid response (RR) activation criteria include a subjective co...
Hospitalization Documentation Unsupervised Machine Learning Risk Assessment Hospital Rapid Response Team Data Mining Humans Nurses Survival Analysis
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Supervised and unsupervised algorithms for bioinformatics and data science.

Progress in biophysics and molecular biology Dec 6, 2019
Bioinformatics refers to an ever evolving huge field of research based on millions of algorithms, designated to several data banks. Such algorithms are either supervised or unsupervised. In this article, a detailed overview of the supervised and unsu...
Supervised Machine Learning Data Science Computational Biology Unsupervised Machine Learning
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Deep Learning Approach for Epileptic Focus Localization.

IEEE transactions on biomedical circuits and systems Dec 2, 2019
The task of epileptic focus localization receives great attention due to its role in an effective epileptic surgery. The clinicians highly depend on the intracranial EEG data to make a surgical decision related to epileptic subjects suffering from un...
Seizures Signal Processing, Computer-Assisted Humans Algorithms Deep Learning Unsupervised Machine Learning Electroencephalography Epilepsy
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CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data.

PLoS computational biology Dec 2, 2019
Quantifying cell-type proportions and their corresponding gene expression profiles in tissue samples would enhance understanding of the contributions of individual cell types to the physiological states of the tissue. Current approaches that address ...
Computer Simulation Gene Expression Profiling Transcriptome Humans Unsupervised Machine Learning Computational Biology Leukocytes Sequence Analysis, RNA Pattern Recognition, Automated Cell Line Databases, Nucleic Acid
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Robust blind spectral unmixing for fluorescence microscopy using unsupervised learning.

PloS one Dec 2, 2019
Due to the overlapping emission spectra of fluorophores, fluorescence microscopy images often have bleed-through problems, leading to a false positive detection. This problem is almost unavoidable when the samples are labeled with three or more fluor...
Fluorescent Dyes Cattle Pulmonary Artery Cell Nucleus Microscopy, Fluorescence Photons Microscopy, Confocal Actins Mitochondria Unsupervised Machine Learning Algorithms Endothelial Cells Animals Fluorescence
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Dissection of gene expression datasets into clinically relevant interaction signatures via high-dimensional correlation maximization.

Nature communications Nov 28, 2019
Gene expression is controlled by many simultaneous interactions, frequently measured collectively in biology and medicine by high-throughput technologies. It is a highly challenging task to infer from these data the generating effects and cooperating...
Correlation of Data Data Visualization Unsupervised Machine Learning Gene Expression Datasets as Topic Humans Gene Expression Profiling Algorithms Computational Biology Lymphoma, Large B-Cell, Diffuse
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Limited One-time Sampling Irregularity Map (LOTS-IM) for Automatic Unsupervised Assessment of White Matter Hyperintensities and Multiple Sclerosis Lesions in Structural Brain Magnetic Resonance Images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Nov 27, 2019
We present the application of limited one-time sampling irregularity map (LOTS-IM): a fully automatic unsupervised approach to extract brain tissue irregularities in magnetic resonance images (MRI), for quantitatively assessing white matter hyperinte...
Image Interpretation, Computer-Assisted White Matter Humans Unsupervised Machine Learning Sensitivity and Specificity Brain Mapping Multiple Sclerosis Disease Progression Image Processing, Computer-Assisted Magnetic Resonance Imaging
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Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance.

Scientific reports Nov 22, 2019
Identification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to class...
Unsupervised Machine Learning Brain Mapping Electroencephalography Retrospective Studies Sensitivity and Specificity Cognition Task Performance and Analysis Evoked Potentials Verbal Behavior Electrocorticography Epilepsy Memory, Short-Term Datasets as Topic Algorithms Brain Biomedical Engineering Humans Electrodes, Implanted Electrophysiological Phenomena
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Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: Application to epilepsy lesion screening.

Medical image analysis Nov 21, 2019
In this study, we propose a novel anomaly detection model targeting subtle brain lesions in multiparametric MRI. To compensate for the lack of annotated data adequately sampling the heterogeneity of such pathologies, we cast this problem as an outlie...
Humans Aged Adult Neural Networks, Computer Female Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Datasets as Topic Male Unsupervised Machine Learning Middle Aged Epilepsy Adolescent
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Unsupervised feature selection algorithm for multiclass cancer classification of gene expression RNA-Seq data.

Genomics Nov 20, 2019
This paper presents a Grouping Genetic Algorithm (GGA) to solve a maximally diverse grouping problem. It has been applied for the classification of an unbalanced database of 801 samples of gene expression RNA-Seq data in 5 types of cancer. The sample...
Neoplasms RNA-Seq Gene Expression Regulation, Neoplastic Unsupervised Machine Learning Humans
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