AIMC Topic: Machine Learning

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Machine learning algorithms based on signals from a single wearable inertial sensor can detect surface- and age-related differences in walking.

Journal of biomechanics
The aim of this study was to investigate if a machine learning algorithm utilizing triaxial accelerometer, gyroscope, and magnetometer data from an inertial motion unit (IMU) could detect surface- and age-related differences in walking. Seventeen old...

Federated learning of predictive models from federated Electronic Health Records.

International journal of medical informatics
BACKGROUND: In an era of "big data," computationally efficient and privacy-aware solutions for large-scale machine learning problems become crucial, especially in the healthcare domain, where large amounts of data are stored in different locations an...

Image analysis and machine learning for detecting malaria.

Translational research : the journal of laboratory and clinical medicine
Malaria remains a major burden on global health, with roughly 200 million cases worldwide and more than 400,000 deaths per year. Besides biomedical research and political efforts, modern information technology is playing a key role in many attempts a...

A Machine Learning Approach to Identifying Placebo Responders in Late-Life Depression Trials.

The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry
OBJECTIVE: Despite efforts to identify characteristics associated with medication-placebo differences in antidepressant trials, few consistent findings have emerged to guide participant selection in drug development settings and differential therapeu...

Deep Convolutional Neural Networks for breast cancer screening.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Radiologists often have a hard time classifying mammography mass lesions which leads to unnecessary breast biopsies to remove suspicions and this ends up adding exorbitant expenses to an already burdened patient and health c...

Machine learning techniques for medical diagnosis of diabetes using iris images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Complementary and alternative medicine techniques have shown their potential for the treatment and diagnosis of chronical diseases like diabetes, arthritis etc. On the same time digital image processing techniques for diseas...

Combining machine learning models of in vitro and in vivo bioassays improves rat carcinogenicity prediction.

Regulatory toxicology and pharmacology : RTP
In vitro genotoxicity bioassays are cost-efficient methods of assessing potential carcinogens. However, many genotoxicity bioassays are inappropriate for detecting chemicals eliciting non-genotoxic mechanisms, such as tumour promotion, this necessita...

Max-margin weight learning for medical knowledge network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The application of medical knowledge strongly affects the performance of intelligent diagnosis, and method of learning the weights of medical knowledge plays a substantial role in probabilistic graphical models (PGMs). The p...

Using convolutional neural networks to estimate time-of-flight from PET detector waveforms.

Physics in medicine and biology
Although there have been impressive strides in detector development for time-of-flight positron emission tomography, most detectors still make use of simple signal processing methods to extract the time-of-flight information from the detector signals...

Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer's disease.

Translational research : the journal of laboratory and clinical medicine
Alzheimer's disease (AD) is a major neurodegenerative disease and the most common cause of dementia. Currently, no treatment exists to slow down or stop the progression of AD. There is converging belief that disease-modifying treatments should focus ...