Browse Articles

The authors test various machine learning models to improve the accuracy and efficiency of pneumonia diagnosis from X-ray images.

The authors use machine learning and electroencephalogram data to propose a method for improving epilepsy diagnosis.

The authors use the Census Household Pulse Survey issued by the US Census Bureau to examine the prevalence of stress and depression among people across socioeconomic statuses.

It can be challenging for the general public to understand data on medical risk. Weseley-Jones and Mordechai tackle this issue by conducting a survey to assess people's skill and comfort with understanding medical risk information in percentage and frequency formats.

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder in the U.S., second only to Alzheimer’s disease. Current diagnostic methods are often inefficient and dependent on clinical exams. This study explored using machine and deep learning to enhance PD diagnosis by analyzing spiral drawings affected by hand tremors, a common PD symptom.

Artificial sweeteners are rising in popularity, in part due to the influence of social media platforms like YouTube. However, YouTube commenters often repeat information about artificial sweeteners that is not supported by scientific research. To investigate how misinformation about sweeteners spreads through social media, Kim and Yoo conduct a content analysis of YouTube comments to reveal how many comments repeat misinformation about artificial sweeteners' effects.

The authors compare neuroimaging datasets to identify potential new biomarkers for earlier detection of Alzheimer's disease.

The authors test electrocardiography biofeedback as a treatment for individuals with Postural Orthostatic Tachycardia Syndrome.

The authors use pictures of candy wrappers and neural networks to improve nutritional accuracy of diet-tracking apps.

In this article the authors look at sugar consumption and the relationship to productivity in the work/labor force.