AIMC Topic:
Predictive Value of Tests
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Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI.
Magnetic resonance imaging
May 3, 2019
PURPOSE: To predict the neoadjuvant chemoradiation therapy (CRT) response in patients with locally advanced rectal cancer (LARC) using radiomics and deep learning based on pre-treatment MRI and a mid-radiation follow-up MRI taken 3-4 weeks after the ...
Development and Validation of Machine Learning Models in Prediction of Remission in Patients With Moderate to Severe Crohn Disease.
JAMA network open
May 3, 2019
IMPORTANCE: Biological therapies have revolutionized inflammatory bowel disease management, but many patients do not respond to biological monotherapy. Identification of likely responders could reduce costs and delays in remission.
Effect of Tube Voltage on Diagnostic Performance of Fractional Flow Reserve Derived From Coronary CT Angiography With Machine Learning: Results From the MACHINE Registry.
AJR. American journal of roentgenology
Apr 30, 2019
Coronary CT angiography (CCTA)-based methods allow noninvasive estimation of fractional flow reserve (cFFR), recently through use of a machine learning (ML) algorithm (cFFR). However, attenuation values vary according to the tube voltage used, and i...
Efficient learning from big data for cancer risk modeling: A case study with melanoma.
Computers in biology and medicine
Apr 30, 2019
BACKGROUND: Building cancer risk models from real-world data requires overcoming challenges in data preprocessing, efficient representation, and computational performance. We present a case study of a cloud-based approach to learning from de-identifi...
Prediction of Alzheimer's disease dementia with MRI beyond the short-term: Implications for the design of predictive models.
NeuroImage. Clinical
Apr 30, 2019
Magnetic resonance imaging (MRI) volumetric measures have become a standard tool for the detection of incipient Alzheimer's Disease (AD) dementia in mild cognitive impairment (MCI). Focused on providing an earlier and more accurate diagnosis, sophist...
Predicting coronary artery disease: a comparison between two data mining algorithms.
BMC public health
Apr 29, 2019
BACKGROUND: Cardiovascular diseases (CADs) are the first leading cause of death across the world. World Health Organization has estimated that morality rate caused by heart diseases will mount to 23 million cases by 2030. Hence, the use of data minin...
To Generate an Ensemble Model for Women Thyroid Prediction Using Data Mining Techniques.
Asian Pacific journal of cancer prevention : APJCP
Apr 29, 2019
Objective: The main objective of this paper is to easily identify thyroid symptom for treatment. Methods: In this paper two main techniques are proposed for mining the hidden pattern in the dataset. Ensemble-I and Ensemble- II both are machine learni...
Advanced atherosclerosis imaging by CT: Radiomics, machine learning and deep learning.
Journal of cardiovascular computed tomography
Apr 21, 2019
In the last decade, technical advances in the field of medical imaging significantly improved and broadened the application of coronary CT angiography (CCTA) for the non-invasive assessment of coronary artery disease. Recently, similar breakthroughs ...
New Multiparametric Analysis of Cardiac Dyssynchrony: Machine Learning and Prediction of Response to CRT.
JACC. Cardiovascular imaging
Apr 17, 2019
Cardiac Resynchronization Therapy
Ventricular Dysfunction, Left
Image Interpretation, Computer-Assisted
Treatment Outcome
Predictive Value of Tests
Signal Processing, Computer-Assisted
Heart Failure
Humans
Machine Learning
Decision Support Techniques
Retrospective Studies
Clinical Decision-Making
Ventricular Function, Left
Echocardiography
Electrocardiography
Machine learning algorithms for the detection of spurious white blood cell differentials due to erythrocyte lysis resistance.
Journal of clinical pathology
Apr 16, 2019
AIMS: Red blood cell (RBC) lysis resistance interferes with white blood cell (WBC) count and differential; still, its detection relies on the identification of an abnormal scattergram, and this is not clearly adverted by specific flags in the Beckman...
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