AIMC Topic: Humans

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Prediction of the output factor using machine and deep learning approach in uniform scanning proton therapy.

Journal of applied clinical medical physics
PURPOSE: The purpose of this work is to develop machine and deep learning-based models to predict output and MU based on measured patient quality assurance (QA) data in uniform scanning proton therapy (USPT).

Joint Angle Estimation of a Tendon-Driven Soft Wearable Robot through a Tension and Stroke Measurement.

Sensors (Basel, Switzerland)
The size of a device and its adaptability to human properties are important factors in developing a wearable device. In wearable robot research, therefore, soft materials and tendon transmissions have been utilized to make robots compact and adaptabl...

Deep learning for dermatologists: Part II. Current applications.

Journal of the American Academy of Dermatology
Because of a convergence of the availability of large data sets, graphics-specific computer hardware, and important theoretical advancements, artificial intelligence has recently contributed to dramatic progress in medicine. One type of artificial in...

Supervised Machine Learning: A Brief Primer.

Behavior therapy
Machine learning is increasingly used in mental health research and has the potential to advance our understanding of how to characterize, predict, and treat mental disorders and associated adverse health outcomes (e.g., suicidal behavior). Machine l...

What's new in IBD therapy: An "omics network" approach.

Pharmacological research
The industrial revolution that began in the late 1800s has resulted in dramatic changes in the environment, human lifestyle, dietary habits, social structure, and so on. Almost certainly because this rapid evolution has outpaced the ability of the bo...

Motion artifacts reduction in brain MRI by means of a deep residual network with densely connected multi-resolution blocks (DRN-DCMB).

Magnetic resonance imaging
OBJECTIVE: Magnetic resonance imaging (MRI) acquisition is inherently sensitive to motion, and motion artifact reduction is essential for improving image quality in MRI.

An automated machine learning approach to predict brain age from cortical anatomical measures.

Human brain mapping
The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a m...

Predicting hospital admission for older emergency department patients: Insights from machine learning.

International journal of medical informatics
BACKGROUND: Emergency departments (ED) are a portal of entry into the hospital and are uniquely positioned to influence the health care trajectories of older adults seeking medical attention. Older adults present to the ED with distinct needs and com...

Machine learning on drug-specific data to predict small molecule teratogenicity.

Reproductive toxicology (Elmsford, N.Y.)
Pregnant women are an especially vulnerable population, given the sensitivity of a developing fetus to chemical exposures. However, prescribing behavior for the gravid patient is guided on limited human data and conflicting cases of adverse outcomes ...