AIMC Topic: Case-Control Studies

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Quantitative susceptibility mapping based hybrid feature extraction for diagnosis of Parkinson's disease.

NeuroImage. Clinical
Parkinson's disease is the second most common neurodegenerative disease in the elderly after Alzheimer's disease. The aetiology and pathogenesis of Parkinson's disease (PD) are still unclear, but the loss of dopaminergic cells and the excessive iron ...

Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction.

Journal of healthcare engineering
OBJECTIVE: Achieving accurate prediction of sepsis detection moment based on bedside monitor data in the intensive care unit (ICU). A good clinical outcome is more probable when onset is suspected and treated on time, thus early insight of sepsis ons...

Machine-learning-derived rules set excludes risk of Parkinson's disease in patients with olfactory or gustatory symptoms with high accuracy.

Journal of neurology
BACKGROUND: Chemosensory loss is a symptom of Parkinson's disease starting already at preclinical stages. Their appearance without an identifiable etiology therefore indicates a possible early symptom of Parkinson's disease. Supervised machine-learni...

A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data.

BMC medical genomics
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
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
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
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
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
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
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 ...