AI Medical Compendium Topic:
Cohort Studies

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Automatic identification of atherosclerosis subjects in a heterogeneous MR brain imaging data set.

Magnetic resonance imaging
Carotid-artery atherosclerosis (CA) contributes significantly to overall morbidity and mortality in ischemic stroke. We propose a machine learning technique to automatically identify subjects with CA from a heterogeneous cohort of magnetic resonance ...

Ensemble learning with 3D convolutional neural networks for functional connectome-based prediction.

NeuroImage
The specificity and sensitivity of resting state functional MRI (rs-fMRI) measurements depend on preprocessing choices, such as the parcellation scheme used to define regions of interest (ROIs). In this study, we critically evaluate the effect of bra...

Outcome prediction of out-of-hospital cardiac arrest with presumed cardiac aetiology using an advanced machine learning technique.

Resuscitation
BACKGROUND: Outcome prediction for patients with out-of-hospital cardiac arrest (OHCA) has the possibility to detect patients who could have been potentially saved. Advanced machine learning techniques have recently been developed and employed for cl...

A Machine Learning Approach to Classifying Self-Reported Health Status in a Cohort of Patients With Heart Disease Using Activity Tracker Data.

IEEE journal of biomedical and health informatics
Constructing statistical models using personal sensor data could allow for tracking health status over time, thereby enabling the possibility of early intervention. The goal of this study was to use machine learning algorithms to classify patient-rep...

Natural Language Processing for Identification of Incidental Pulmonary Nodules in Radiology Reports.

Journal of the American College of Radiology : JACR
PURPOSE: To develop natural language processing (NLP) to identify incidental lung nodules (ILNs) in radiology reports for assessment of management recommendations.

Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline.

Nature communications
Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept...

Predicting breast cancer metastasis by using serum biomarkers and clinicopathological data with machine learning technologies.

International journal of medical informatics
BACKGROUND: Approximately 10%-15% of patients with breast cancer die of cancer metastasis or recurrence, and early diagnosis of it can improve prognosis. Breast cancer outcomes may be prognosticated on the basis of surface markers of tumor cells and ...