AIMC Topic: Middle Aged

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Using fMRI and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis.

NeuroImage. Clinical
Cognitive behavioural therapy for psychosis (CBTp) involves helping patients to understand and reframe threatening appraisals of their psychotic experiences to reduce distress and increase functioning. Whilst CBTp is effective for many, it is not eff...

Improving Performance of Devanagari Script Input-Based P300 Speller Using Deep Learning.

IEEE transactions on bio-medical engineering
The performance of an existing Devanagari script (DS) input-based P300 speller with conventional machine learning techniques suffers from low information transfer rate (ITR). This occurs due to its required large size of display, i.e., 8 × 8 row-colu...

Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning.

European radiology
OBJECTIVE: Accurate detection and segmentation of organs at risks (OARs) in CT image is the key step for efficient planning of radiation therapy for nasopharyngeal carcinoma (NPC) treatment. We develop a fully automated deep-learning-based method (te...

Accuracy of an artificial neural network for detecting a regional abnormality in myocardial perfusion SPECT.

Annals of nuclear medicine
OBJECTIVES: The patient-based diagnosis with an artificial neural network (ANN) has shown potential utility for the detection of coronary artery disease; however, the region-based accuracy of the detected regions has not been fully evaluated. The aim...

Diagnosis of T-cell-mediated kidney rejection in formalin-fixed, paraffin-embedded tissues using RNA-Seq-based machine learning algorithms.

Human pathology
Molecular diagnosis is being increasingly used in transplant pathology to render more objective and quantitative determinations that also provide mechanistic and prognostic insights. This study performed RNA-Seq on biopsies from kidneys with stable f...

Automatic detection of oral and pharyngeal phases in swallowing using classification algorithms and multichannel EMG.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
Swallowing is a complex process that involves sequential voluntary and involuntary muscle contractions. Malfunctioning of swallowing related muscles could lead to dysphagia. However, there is a lack of standardized and non-invasive methods that suppo...

Comparison of 2 Natural Language Processing Methods for Identification of Bleeding Among Critically Ill Patients.

JAMA network open
IMPORTANCE: To improve patient safety, health care systems need reliable methods to detect adverse events in large patient populations. Events are often described in clinical notes, rather than structured data, which make them difficult to identify o...

Machine-learning based lipid mediator serum concentration patterns allow identification of multiple sclerosis patients with high accuracy.

Scientific reports
Based on increasing evidence suggesting that MS pathology involves alterations in bioactive lipid metabolism, the present analysis was aimed at generating a complex serum lipid-biomarker. Using unsupervised machine-learning, implemented as emergent s...

Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography.

Journal of gastroenterology
BACKGROUND: Deep learning has become a new trend of image recognition tasks in the field of medicine. We developed an automated gastritis detection system using double-contrast upper gastrointestinal barium X-ray radiography.