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Seasonality of acute kidney injury phenotypes in England: an unsupervised machine learning classification study of electronic health records
BackgroundAcute Kidney Injury (AKI) is a multifactorial condition which presents a substantial burden to healthcare systems. There is limited...
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Reassessing acquired neonatal intestinal diseases using unsupervised machine learning
BackgroundAcquired neonatal intestinal diseases have an array of overlap** presentations and are often labeled under the dichotomous classification...
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Exploring subtypes of multiple sclerosis through unsupervised machine learning of automated fiber quantification
PurposeThis study aimed to subtype multiple sclerosis (MS) patients using unsupervised machine learning on white matter (WM) fiber tracts and...
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Identifying clusters of objective functional impairment in patients with degenerative lumbar spinal disease using unsupervised learning
ObjectivesThe five-repetition sit-to-stand (5R-STS) test was designed to capture objective functional impairment (OFI), and thus provides an...
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Unsupervised stain augmentation enhanced glomerular instance segmentation on pathology images
PurposeIn pathology images, different stains highlight different glomerular structures, so a supervised deep learning-based glomerular instance...
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Combining unsupervised, supervised and rule-based learning: the case of detecting patient allergies in electronic health records
BackgroundData mining of electronic health records (EHRs) has a huge potential for improving clinical decision support and to help healthcare deliver...
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Construction of prediction models for novel subtypes in patients with arteriosclerosis obliterans undergoing endovascular therapy: an unsupervised machine learning study
BackgroundArteriosclerosis obliterans (ASO) is a chronic arterial disease that can lead to critical limb ischemia. Endovascular therapy is...
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Procedure code overutilization detection from healthcare claims using unsupervised deep learning methods
BackgroundFraud, Waste, and Abuse (FWA) in medical claims have a negative impact on the quality and cost of healthcare. A major component of FWA in...
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A remote digital memory composite to detect cognitive impairment in memory clinic samples in unsupervised settings using mobile devices
Remote monitoring of cognition holds the promise to facilitate case-finding in clinical care and the individual detection of cognitive impairment in...
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Unsupervised Machine Learning Revealed that Repeat Transcranial Magnetic Stimulation is More Suitable for Stroke Patients with Statin
IntroductionRepeat transcranial magnetic stimulation (rTMS) demonstrates beneficial effects for stroke patients, though its efficacy varies due to...
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Unsupervised meta-clustering identifies risk clusters in acute myeloid leukemia based on clinical and genetic profiles
BackgroundIncreasingly large and complex biomedical data sets challenge conventional hypothesis-driven analytical approaches, however, data-driven...
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Using unsupervised machine learning to classify behavioral risk markers of bacterial vaginosis
IntroductionThis study used an unsupervised machine learning algorithm, sidClustering and random forests, to identify clusters of risk behaviors of...
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Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart Counts App and smart devices
Previous studies have associated COVID-19 symptoms severity with levels of physical activity. We therefore investigated longitudinal trajectories of...
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Unsupervised machine learning identifies predictive progression markers of IPF
ObjectivesTo identify and evaluate predictive lung imaging markers and their pathways of change during progression of idiopathic pulmonary fibrosis...
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Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging
PurposePatients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics....
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MedFusionGAN: multimodal medical image fusion using an unsupervised deep generative adversarial network
PurposeThis study proposed an end-to-end unsupervised medical fusion generative adversarial network, MedFusionGAN, to fuse computed tomography (CT)...
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Self-supervised category selective attention classifier network for diabetic macular edema classification
AimsThis study aims to develop an advanced model for the classification of Diabetic Macular Edema (DME) using deep learning techniques. Specifically,...
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The evolving diagnosis and classification of CNS hypersomnolence disorders
Purpose of ReviewWe describe the evolution and limitations of current diagnostic criteria and classification systems of CNS hypersomnolence disorders...
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Unsupervised anomaly detection of implausible electronic health records: a real-world evaluation in cancer registries
BackgroundCancer registries collect patient-specific information about cancer diseases. The collected information is verified and made available to...
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Unsupervised item response theory models for assessing sample heterogeneity in patient-reported outcomes measures
PurposeUnsupervised item-response theory (IRT) models such as polytomous IRT based on recursive partitioning (IRTrees) and mixture IRT (MixIRT)...
