AIMC Topic: Machine Learning

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Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning.

Frontiers in immunology
Expression of CCR5 and its cognate ligands have been implicated in COVID-19 pathogenesis, consequently therapeutics directed against CCR5 are being investigated. Here, we explored the role of CCR5 and its ligands across the immunologic spectrum of CO...

Automated Detection and Diameter Estimation for Mouse Mesenteric Artery Using Semantic Segmentation.

Journal of vascular research
BACKGROUND: Pressurized myography is useful for the assessment of small artery structures and function. However, this procedure requires technical expertise for sample preparation and effort to choose an appropriate sized artery. In this study, we de...

Replication of machine learning methods to predict treatment outcome with antidepressant medications in patients with major depressive disorder from STAR*D and CAN-BIND-1.

PloS one
OBJECTIVES: Antidepressants are first-line treatments for major depressive disorder (MDD), but 40-60% of patients will not respond, hence, predicting response would be a major clinical advance. Machine learning algorithms hold promise to predict trea...

Non-invasive thyroid detection based on electroglottogram signal using machine learning classifiers.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Thyroid is a butterfly shaped gland located in the neck region. Hormones are secreted by the thyroid gland that is responsible for various functions that maintain metabolism of the body. The variance in secretion of the hormones causes disorders such...

Factors to improve distress and fatigue in Cancer survivorship; further understanding through text analysis of interviews by machine learning.

BMC cancer
BACKGROUND: From patient-reported surveys and individual interviews by health care providers, we attempted to identify the significant factors related to the improvement of distress and fatigue for cancer survivors by text analysis with machine learn...

A machine learning approach for the prediction of overall deceased donor organ yield.

Surgery
BACKGROUND: Optimizing organ yield (number of organs transplanted per donor) is a potentially modifiable way to increase the number of organs available for transplant. Models to predict the expected deceased donor organ yield have been developed base...

Learning to estimate the fiber orientation distribution function from diffusion-weighted MRI.

NeuroImage
Estimation of white matter fiber orientation distribution function (fODF) is the essential first step for reliable brain tractography and connectivity analysis. Most of the existing fODF estimation methods rely on sub-optimal physical models of the d...

Clinical decision support algorithm based on machine learning to assess the clinical response to anti-programmed death-1 therapy in patients with non-small-cell lung cancer.

European journal of cancer (Oxford, England : 1990)
OBJECTIVE: Anti-programmed death (PD)-1 therapy confers sustainable clinical benefits for patients with non-small-cell lung cancer (NSCLC), but only some patients respond to the treatment. Various clinical characteristics, including the PD-ligand 1 (...

WDA: An Improved Wasserstein Distance-Based Transfer Learning Fault Diagnosis Method.

Sensors (Basel, Switzerland)
With the growth of computing power, deep learning methods have recently been widely used in machine fault diagnosis. In order to realize highly efficient diagnosis accuracy, people need to know the detailed health condition of collected signals from ...

Deep LSTM-Based Transfer Learning Approach for Coherent Forecasts in Hierarchical Time Series.

Sensors (Basel, Switzerland)
Hierarchical time series is a set of data sequences organized by aggregation constraints to represent many real-world applications in research and the industry. Forecasting of hierarchical time series is a challenging and time-consuming problem owing...