Summary of Generating Medical Screening Questionnaires Through Analysis Of Social Media Data, by Ortal Ashkenazi et al.
Generating medical screening questionnaires through analysis of social media data
by Ortal Ashkenazi, Elad Yom-Tov, Liron Vardi David
First submitted to arxiv on: 17 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method aims to generate screening questionnaires for medical conditions from social media posts, potentially improving the diagnostic aid process. The approach identifies relevant users through dedicated patient groups and a control group, then uses decision rules generated from pre-diagnosis posts to differentiate between groups. These rules are validated by correlating them with scores given by medical doctors to hypothetical cases. The method is demonstrated for three conditions (endometriosis, lupus, and gout) using Reddit data, with average Pearson’s correlation coefficients ranging from 0.27 to 0.58. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new way to create screening questionnaires for medical conditions by analyzing social media posts. It uses special groups on Reddit to find people who have the condition and those who don’t, then looks at what they wrote before they were diagnosed. The researchers use this information to make rules that can help doctors diagnose the condition correctly. They tested their method with three conditions (endometriosis, lupus, and gout) and got pretty good results. This could be a helpful tool for diagnosing diseases. |
