AIMC Topic: Machine Learning

Clear Filters Showing 33291 to 33300 of 34417 articles

Natural language processing and machine learning to identify alcohol misuse from the electronic health record in trauma patients: development and internal validation.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Alcohol misuse is present in over a quarter of trauma patients. Information in the clinical notes of the electronic health record of trauma patients may be used for phenotyping tasks with natural language processing (NLP) and supervised ma...

Machine learning-based prediction of clinical pain using multimodal neuroimaging and autonomic metrics.

Pain
Although self-report pain ratings are the gold standard in clinical pain assessment, they are inherently subjective in nature and significantly influenced by multidimensional contextual variables. Although objective biomarkers for pain could substant...

Hybrid 11C-MET PET/MRI Combined With "Machine Learning" in Glioma Diagnosis According to the Revised Glioma WHO Classification 2016.

Clinical nuclear medicine
PURPOSE: With the advent of the revised WHO classification from 2016, molecular features, including isocitrate dehydrogenase (IDH) mutation have become important in glioma subtyping. This pilot trial analyzed the potential for C-methionine (MET) PET/...

Whole-brain structural magnetic resonance imaging-based classification of primary dysmenorrhea in pain-free phase: a machine learning study.

Pain
To develop a machine learning model to investigate the discriminative power of whole-brain gray-matter (GM) images derived from primary dysmenorrhea (PDM) women and healthy controls (HCs) during the pain-free phase and further evaluate the predictive...

Artificial Intelligence-Assisted Gastroenterology- Promises and Pitfalls.

The American journal of gastroenterology
Technological advances in artificial intelligence (AI) represent an enticing opportunity to benefit gastroenterological practice. Moreover, AI, through machine or deep learning, permits the ability to develop predictive models from large datasets. Po...

Using Machine Learning to Assess Physician Competence: A Systematic Review.

Academic medicine : journal of the Association of American Medical Colleges
PURPOSE: To identify the different machine learning (ML) techniques that have been applied to automate physician competence assessment and evaluate how these techniques can be used to assess different competence domains in several medical specialties...

Microelectrode Recordings Validate the Clinical Visualization of Subthalamic-Nucleus Based on 7T Magnetic Resonance Imaging and Machine Learning for Deep Brain Stimulation Surgery.

Neurosurgery
BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a proven and effective therapy for the management of the motor symptoms of Parkinson's disease (PD). While accurate positioning of the stimulating electrode is critical for ...

Development and application of a comprehensive machine learning program for predicting molecular biochemical and pharmacological properties.

Physical chemistry chemical physics : PCCP
We establish a comprehensive quantitative structure-activity relationship (QSAR) model termed AlphaQ through the machine learning algorithm to associate the fully quantum mechanical molecular descriptors with various biochemical and pharmacological p...

Digital transformation in healthcare - architectures of present and future information technologies.

Clinical chemistry and laboratory medicine
Healthcare providers all over the world are faced with a single challenge: the need to improve patient outcomes while containing costs. Drivers include an increasing demand for chronic disease management for an aging population, technological advance...