Detecting health misinformation in online health communities: Incorporating behavioral features into machine learning based approaches

Counteracting Online Health Misinformation: A Qualitative Study

Background: The internet and social media are considered as new tools to seek health information. Health misinformation is defined as a health-related claim of fact that is false due to the lack of scientific reliable evidence. These kinds of information are produced both intentionally or non-intentionally and can impose negative impact on the population health. This study was aimed to explore ...

Approaches to Integrating Physical Health Services into Behavioral Health Organizations

Incorporating Global Visual Features into Attention-based Neural Machine Translation

We introduce multi-modal, attentionbased Neural Machine Translation (NMT) models which incorporate visual features into different parts of both the encoder and the decoder. Global image features are extracted using a pre-trained convolutional neural network and are incorporated (i) as words in the source sentence, (ii) to initialise the encoder hidden state, and (iii) as additional data to init...

Personas in online health communities

Many researchers and practitioners use online health communities (OHCs) to influence health behavior and provide patients with social support. One of the biggest challenges in this approach, however, is the rate of attrition. OHCs face similar problems as other social media platforms where user migration happens unless tailored content and appropriate socialization is supported. To provide tail...

Machine Learning Approaches for Detecting Diabetic Retinopathy from Clinical and Public Health Records

INTRODUCTION Annual eye examinations are recommended for diabetic patients in order to detect diabetic retinopathy and other eye conditions that arise from diabetes. Medically underserved urban communities in the US have annual screening rates that are much lower than the national average and could benefit from informatics approaches to identify unscreened patients most at risk of developing re...