AIMC Topic: Case-Control Studies
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A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data.
BMC medical genomics
Oct 30, 2019
BACKGROUND: Dementia with Lewy bodies (DLB) is the second most common subtype of neurodegenerative dementia in humans following Alzheimer's disease (AD). Present clinical diagnosis of DLB has high specificity and low sensitivity and finding potential...
Anti-varicella Zoster Virus IgG and hsCRP Levels Correlate with Progression of Coronary Artery Atherosclerosis.
Iranian journal of allergy, asthma, and immunology
Oct 23, 2019
The relationship between high levels of anti-Varicella Zoster Virus (VZV) IgG in cerebrospinal fluid (CSF) and cerebrovascular atherosclerosis commends a possible similar association in other vessels. We aimed to investigate the association of VZV-se...
Effects of robotic neurorehabilitation through lokomat plus virtual reality on cognitive function in patients with traumatic brain injury: A retrospective case-control study.
The International journal of neuroscience
Oct 15, 2019
Traumatic brain injury (TBI) is a clinical condition characterized by damage due to a mechanical physical event, which has a devastating impact on both the patient and his/her family. The purpose of this study is to evaluate the effects of robotic n...
A comparative study of machine learning classifiers for risk prediction of asthma disease.
Photodiagnosis and photodynamic therapy
Oct 12, 2019
Asthma is a chronic disease characterized by wheezing, chest tightening and difficulty in breathing due to inflammation of lung airways. Early risk prediction of asthma is crucial for proper and effective management. This study presents the use of ma...
Diagnosing brain tumours by routine blood tests using machine learning.
Scientific reports
Oct 9, 2019
Routine blood test results are assumed to contain much more information than is usually recognised even by the most experienced clinicians. Using routine blood tests from 15,176 neurological patients we built a machine learning predictive model for t...
Fully automated 3D segmentation and separation of multiple cervical vertebrae in CT images using a 2D convolutional neural network.
Computer methods and programs in biomedicine
Oct 4, 2019
BACKGROUND AND OBJECTIVE: We investigated a novel method using a 2D convolutional neural network (CNN) to identify superior and inferior vertebrae in a single slice of CT images, and a post-processing for 3D segmentation and separation of cervical ve...
Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study.
The Lancet. Oncology
Oct 4, 2019
BACKGROUND: Upper gastrointestinal cancers (including oesophageal cancer and gastric cancer) are the most common cancers worldwide. Artificial intelligence platforms using deep learning algorithms have made remarkable progress in medical imaging but ...
Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis.
Human brain mapping
Oct 1, 2019
Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity ...
Validation of an alcohol misuse classifier in hospitalized patients.
Alcohol (Fayetteville, N.Y.)
Sep 28, 2019
BACKGROUND: Current modes of identifying alcohol misuse in hospitalized patients rely on self-report questionnaires and diagnostic codes that have limitations, including low sensitivity. Information in the clinical notes of the electronic health reco...
Improving documentation of presenting problems in the emergency department using a domain-specific ontology and machine learning-driven user interfaces.
International journal of medical informatics
Sep 27, 2019
OBJECTIVES: To determine the effect of a domain-specific ontology and machine learning-driven user interfaces on the efficiency and quality of documentation of presenting problems (chief complaints) in the emergency department (ED).
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