AIMC Topic: Machine Learning

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Comparison of 2 Natural Language Processing Methods for Identification of Bleeding Among Critically Ill Patients.

JAMA network open
IMPORTANCE: To improve patient safety, health care systems need reliable methods to detect adverse events in large patient populations. Events are often described in clinical notes, rather than structured data, which make them difficult to identify o...

Machine-learning based lipid mediator serum concentration patterns allow identification of multiple sclerosis patients with high accuracy.

Scientific reports
Based on increasing evidence suggesting that MS pathology involves alterations in bioactive lipid metabolism, the present analysis was aimed at generating a complex serum lipid-biomarker. Using unsupervised machine-learning, implemented as emergent s...

Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis.

Radiation oncology (London, England)
BACKGROUND: To investigate the effect of machine learning methods on predicting the Overall Survival (OS) for non-small cell lung cancer based on radiomics features analysis.

Health system measurement: Harnessing machine learning to advance global health.

PloS one
BACKGROUND: Further improvements in population health in low- and middle-income countries demand high-quality care to address an increasingly complex burden of disease. Health facility surveys provide an important but costly source of information on ...

Towards a modular decision support system for radiomics: A case study on rectal cancer.

Artificial intelligence in medicine
Following the personalized medicine paradigm, there is a growing interest in medical agents capable of predicting the effect of therapies on patients, by exploiting the amount of data that is now available for each patient. In disciplines like oncolo...

Prediction of spinal curve progression in Adolescent Idiopathic Scoliosis using Random Forest regression.

Computers in biology and medicine
BACKGROUND: The progression of the spinal curve represents one of the major concerns in the assessment of Adolescent Idiopathic Scoliosis (AIS). The prediction of the shape of the spine from the first visit could guide the management of AIS and provi...

Potential identification of vitamin B6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods.

Scientific reports
We investigated whether machine learning methods could potentially identify a subgroup of persons with autism spectrum disorder (ASD) who show vitamin B6 responsiveness by selected phenotype variables. We analyzed the existing data from our intervent...

Early Expression Detection via Online Multi-Instance Learning With Nonlinear Extension.

IEEE transactions on neural networks and learning systems
Video-based facial expression recognition has received substantial attention over the past decade, while early expression detection (EED) is still a relatively new and challenging problem. The goal of EED is to identify an expression as quickly as po...

Detecting drug-resistant tuberculosis in chest radiographs.

International journal of computer assisted radiology and surgery
PURPOSE: Tuberculosis is a major global health threat claiming millions of lives each year. While the total number of tuberculosis cases has been decreasing over the last years, the rise of drug-resistant tuberculosis has reduced the chance of contro...

Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials.

Journal of neural engineering
OBJECTIVE: Steady-state visual evoked potentials (SSVEPs) are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli. SSVEPs are robust signals measurable in the electroencephalogram (E...