AIMC Topic: Machine Learning

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Correlating exhaled aerosol images to small airway obstructive diseases: A study with dynamic mode decomposition and machine learning.

PloS one
BACKGROUND: Exhaled aerosols from lungs have unique patterns, and their variation can be correlated to the underlying lung structure and associated abnormities. However, it is challenging to characterize such aerosol patterns and differentiate their ...

Machine learning for prediction of sustained opioid prescription after anterior cervical discectomy and fusion.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: The severity of the opioid epidemic has increased scrutiny of opioid prescribing practices. Spine surgery is a high-risk episode for sustained postoperative opioid prescription.

Machine learning vs addiction therapists: A pilot study predicting alcohol dependence treatment outcome from patient data in behavior therapy with adjunctive medication.

Journal of substance abuse treatment
BACKGROUND AND OBJECTIVES: Clinical staff providing addiction treatment predict patient outcome poorly. Prognoses based on linear statistics are rarely replicated. Addiction is a complex non-linear behavior. Incorporating non-linear models, Machine L...

Multi-task exclusive relationship learning for alzheimer's disease progression prediction with longitudinal data.

Medical image analysis
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions. Currently, many multi-task learning approaches have been proposed to predict the disease progression at the earl...

A robust outlier control framework for classification designed with family of homotopy loss function.

Neural networks : the official journal of the International Neural Network Society
We propose a new homotopy loss, where practitioners can tune the parameter to derive different loss functions such as l-norm loss, logarithmic loss, Geman-Reynolds loss, Geman-McClure loss and correntropy-based loss. Moreover, we illustrate that the ...

Machine learning in radiology.

Journal of medical imaging and radiation oncology

Using Machine Learning To Predict Antimicrobial MICs and Associated Genomic Features for Nontyphoidal .

Journal of clinical microbiology
Nontyphoidal species are the leading bacterial cause of foodborne disease in the United States. Whole-genome sequences and paired antimicrobial susceptibility data are available for strains because of surveillance efforts from public health agencie...

Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers.

Disease markers
INTRODUCTION: Hematological indices including red cell distribution width and neutrophil to lymphocyte ratio are proven to be associated with outcomes of acute coronary syndrome. The usefulness of machine learning techniques in predicting mortality a...