AIMC Topic: Humans

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Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia.

PLoS neglected tropical diseases
The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were dev...

Classification of estrogenic compounds by coupling high content analysis and machine learning algorithms.

PLoS computational biology
Environmental toxicants affect human health in various ways. Of the thousands of chemicals present in the environment, those with adverse effects on the endocrine system are referred to as endocrine-disrupting chemicals (EDCs). Here, we focused on a ...

Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework.

Journal of diabetes research
BACKGROUND: An estimated 425 million people globally have diabetes, accounting for 12% of the world's health expenditures, and the number continues to grow, placing a huge burden on the healthcare system, especially in those remote, underserved areas...

A non-parametric effect-size measure capturing changes in central tendency and data distribution shape.

PloS one
MOTIVATION: Calculating the magnitude of treatment effects or of differences between two groups is a common task in quantitative science. Standard effect size measures based on differences, such as the commonly used Cohen's, fail to capture the treat...

A Study of the Chemistries, Growth Mechanisms, and Antibacterial Properties of Cerium- and Yttrium-Containing Nanoparticles.

ACS biomaterials science & engineering
Under the current climate, physicians prescribe antibiotics for treating bacterial infections, and such a limitation to a single class of drugs is disadvantageous since antibiotic-resistant bacteria have adapted to withstanding their stresses. Antibi...

Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation.

NeuroImage
Virtual delineation of white matter bundles in the human brain is of paramount importance for multiple applications, such as pre-surgical planning and connectomics. A substantial body of literature is related to methods that automatically segment bun...

An immune-related gene signature for determining Ewing sarcoma prognosis based on machine learning.

Journal of cancer research and clinical oncology
PURPOSE: Ewing sarcoma (ES) is one of the most common malignant bone tumors in children and adolescents. The immune microenvironment plays an important role in the development of ES. Here, we developed an optimal signature for determining ES patient ...

Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms.

International journal of medical informatics
OBJECTIVE: This study aims to develop and test a new computer-aided diagnosis (CAD) scheme of chest X-ray images to detect coronavirus (COVID-19) infected pneumonia.

Deep learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: To enable accurate magnetic resonance imaging (MRI)-based dose calculations, synthetic computed tomography (sCT) images need to be generated. We aim at assessing the feasibility of dose calculations from MRI acquired with a he...

Assessing and mitigating the effects of class imbalance in machine learning with application to X-ray imaging.

International journal of computer assisted radiology and surgery
PURPOSE: Machine learning (ML) algorithms are well known to exhibit variations in prediction accuracy when provided with imbalanced training sets typically seen in medical imaging (MI) due to the imbalanced ratio of pathological and normal cases. Thi...