Using machine learning algorithms to filter through social media postings allows medical researchers to gain insight into patients' experiences. So in experiences that are often overlooked or difficult to obtain when relying primarily on data from medical records and medical studies that can take years to complete. This is the conclusion reached by researchers at the Perelman School of Medicine at the University of Pennsylvania .

Cost-effective alternative

According to researcher Graciela Gonzalez-Hernandez, collecting mountains of data from social media is inexpensive, requires no burden on participants, and can be done in real time. It also makes it easier to capture the voices of underrepresented populations that are often not represented in biomedical testing and traditional cohort studies. One of those areas, he said, is the way healthcare providers report prescription drug problems to the FDA Adverse Event Reporting System .

According to Gonzalez-Hernandez, those responsible report what they consider important, for example that serious events are overrepresented. On the other hand, disturbing side effects that are of great importance to patients and can, among other things, lead to non-compliance with prescribed medication may be underrepresented. A recently published study evaluated media postings to learn more about the effects of buprenorphine, a drug that helps opioid users withdraw. Research shows concern among registered users of Reddit. People who have used fentanyl are reported to have experienced extreme withdrawal symptoms. According to co-author Abeed Sarker, many people are venting their frustration about this. Additionally, a large increase in writing about fentanyl and withdrawal has been noted over the past seven years.

Medicine: More knowledge about breast cancer

In France, researchers have developed machine learning algorithms to help healthcare providers better care for women with breast cancer. The program identifies topics that patients raise http://cancerdusein.org European Organization for Cancer Research and Care to measure quality is used. It became apparent that topics that were of concern to women were not covered by the questionnaire.

Some researchers are currently exploring how to combine data from social media postings with more traditional approaches. Su Golder from the University of York is currently reviewing the medical literature on the side effects of the HPV vaccine against the human papillona virus. WebMD forums . The goal is to find out what is being said online about the vaccinations. According to the scientist, it is important to research what worries people.

Data for suicide statistics

Researchers at the Centers for Disease Control and Prevention and the Georgia Institute of Technology have combined data from social media with more traditional epidemiological sources. The aim was to improve the accuracy and timing of estimates for national suicide numbers. Suicide statistics are currently being evaluated using death certificates from more than 2,000 pathologists. It may take more than a year to compile these statistics. Therefore, it becomes difficult to plan an effective prevention program.

By adding social media postings and other data, the researchers were able to accurately estimate national suicide numbers for each week. The algorithm combs through suicide postings on Reddit, Twitter and Tumblr. Trends on Google and YouTube, visits to the emergency room due to suicide attempts and thoughts are evaluated. Additionally, the National Suicide Prevention Lifeline and the National Poison Data System are evaluated.

That could also be of interest

Ukraine: Body was not placed on the street for media reports. A Facebook post that acts as a fact check is intended to show that a body was positioned specifically for the media. But there is something different behind the recording. Continue reading …


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