AIMC Topic: Machine Learning

Clear Filters Showing 25221 to 25230 of 34417 articles

Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants.

Scientific reports
Current public health guidelines on physical activity and sleep duration are limited by a reliance on subjective self-reported evidence. Using data from simple wrist-worn activity monitors, we developed a tailored machine learning model, using balanc...

High-Grade Serous Ovarian Cancer: Use of Machine Learning to Predict Abdominopelvic Recurrence on CT on the Basis of Serial Cancer Antigen 125 Levels.

Journal of the American College of Radiology : JACR
PURPOSE: The aim of this study was to use machine learning to predict abdominal recurrence on CT on the basis of serial cancer antigen 125 (CA125) levels in patients with advanced high-grade serous ovarian cancer on surveillance.

Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To train and evaluate a very deep dilated residual network (DD-ResNet) for fast and consistent auto-segmentation of the clinical target volume (CTV) for breast cancer (BC) radiotherapy with big data.

Classifying medical relations in clinical text via convolutional neural networks.

Artificial intelligence in medicine
Deep learning research on relation classification has achieved solid performance in the general domain. This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinic...

Phrase mining of textual data to analyze extracellular matrix protein patterns across cardiovascular disease.

American journal of physiology. Heart and circulatory physiology
Extracellular matrix (ECM) proteins have been shown to play important roles regulating multiple biological processes in an array of organ systems, including the cardiovascular system. Using a novel bioinformatics text-mining tool, we studied six cate...

Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase.

Chemical biology & drug design
In this study, we describe the development of new machine learning models to predict inhibition of the enzyme 3-dehydroquinate dehydratase (DHQD). This enzyme is the third step of the shikimate pathway and is responsible for the synthesis of chorisma...

Improving resolution of MR images with an adversarial network incorporating images with different contrast.

Medical physics
PURPOSE: The routine MRI scan protocol consists of multiple pulse sequences that acquire images of varying contrast. Since high frequency contents such as edges are not significantly affected by image contrast, down-sampled images in one contrast may...

A cure-rate model for Q-learning: Estimating an adaptive immunosuppressant treatment strategy for allogeneic hematopoietic cell transplant patients.

Biometrical journal. Biometrische Zeitschrift
Cancers treated by transplantation are often curative, but immunosuppressive drugs are required to prevent and (if needed) to treat graft-versus-host disease. Estimation of an optimal adaptive treatment strategy when treatment at either one of two st...

Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: Although advanced MRI techniques are increasingly available, imaging differentiation between glioblastoma and primary central nervous system lymphoma (PCNSL) is sometimes confusing. We aimed to evaluate the performance of image classificatio...

Structured radiology reporting on an institutional level-benefit or new administrative burden?

Annals of the New York Academy of Sciences
Significant technical advances have been made in radiology since the first discovery of X-rays. Diagnostic techniques have become more and more complex, workflows have been digitized, and data production has increased exponentially. However, the radi...