AI Medical Compendium Topic:
Unsupervised Machine Learning

Clear Filters Showing 731 to 740 of 758 articles

L1000FWD: fireworks visualization of drug-induced transcriptomic signatures.

Bioinformatics (Oxford, England)
MOTIVATION: As part of the NIH Library of Integrated Network-based Cellular Signatures program, hundreds of thousands of transcriptomic signatures were generated with the L1000 technology, profiling the response of human cell lines to over 20 000 sma...

Retinal Nerve Fiber Layer Features Identified by Unsupervised Machine Learning on Optical Coherence Tomography Scans Predict Glaucoma Progression.

Investigative ophthalmology & visual science
PURPOSE: To apply computational techniques to wide-angle swept-source optical coherence tomography (SS-OCT) images to identify novel, glaucoma-related structural features and improve detection of glaucoma and prediction of future glaucomatous progres...

The New Possibilities from "Big Data" to Overlooked Associations Between Diabetes, Biochemical Parameters, Glucose Control, and Osteoporosis.

Current osteoporosis reports
PURPOSE OF REVIEW: To review current practices and technologies within the scope of "Big Data" that can further our understanding of diabetes mellitus and osteoporosis from large volumes of data. "Big Data" techniques involving supervised machine lea...

Development and Evaluation of an Automated Machine Learning Algorithm for In-Hospital Mortality Risk Adjustment Among Critical Care Patients.

Critical care medicine
OBJECTIVES: Risk adjustment algorithms for ICU mortality are necessary for measuring and improving ICU performance. Existing risk adjustment algorithms are not widely adopted. Key barriers to adoption include licensing and implementation costs as wel...

Using Unsupervised Machine Learning to Identify Subgroups Among Home Health Patients With Heart Failure Using Telehealth.

Computers, informatics, nursing : CIN
This study explored the use of unsupervised machine learning to identify subgroups of patients with heart failure who used telehealth services in the home health setting, and examined intercluster differences for patient characteristics related to me...

Superior arm-movement decoding from cortex with a new, unsupervised-learning algorithm.

Journal of neural engineering
OBJECTIVE: The aim of this work is to improve the state of the art for motor-control with a brain-machine interface (BMI). BMIs use neurological recording devices and decoding algorithms to transform brain activity directly into real-time control of ...

Unsupervised multiple kernel learning for heterogeneous data integration.

Bioinformatics (Oxford, England)
MOTIVATION: Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of appli...

Digital radiography image denoising using a generative adversarial network.

Journal of X-ray science and technology
Statistical noise may degrade the x-ray image quality of digital radiography (DR) system. This corruption can be alleviated by extending exposure time of detectors and increasing the intensity of radiation. However, in some instances, such as the sec...

Improving Layman Readability of Clinical Narratives with Unsupervised Synonym Replacement.

Studies in health technology and informatics
We report on the development and evaluation of a prototype tool aimed to assist laymen/patients in understanding the content of clinical narratives. The tool relies largely on unsupervised machine learning applied to two large corpora of unlabeled te...

Mapping Patient Trajectories using Longitudinal Extraction and Deep Learning in the MIMIC-III Critical Care Database.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Electronic Health Records (EHRs) contain a wealth of patient data useful to biomedical researchers. At present, both the extraction of data and methods for analyses are frequently designed to work with a single snapshot of a patient's record. Health ...