Using Sentiment Analysis to Improve Patient Outcomes

  1 m, 11 s

I always love to see text analytics and natural language processing gaining more attention. So this new paper published in the International Journal of Scientific Research and Review immediately caught my eye. In it, researchers from Sant Baba Bhag Singh University examine how sentiment analysis helps medical researchers improve treatments and outcomes for diabetes patients.

To do this, the SBBSU team dug into how researchers and organizations have previously used sentiment analysis to understand patient sentiment on social media channels, including Twitter, Facebook, and Instagram. Specifically, they dove into patient conversations surrounding Type-1 and Type-2 Diabetes treatment, drugs, and diet practices.

The medical research team hoped to offer healthcare institutions a new perspective on how HCPs can enhance treatment and services, both for diabetes patients and in general. The authors conclude that using sentiment analysis to examine social media data is an effective avenue for HCPs to gain new and better insights into how people experience their conditions and treatments.

(Also, it’s pretty cool to see that many of the studies referenced in the paper use the Lexalytics Intelligence Platform for their sentiment analysis “workbench”.)

The researchers argue that biomedical institutions armed with sentiment analysis techniques can use social media as a viable platform for patient inquiries and dialogue. Further, they hope that others can build on the analysis techniques outlined in their paper to create preventive measures and behavioural incentives for diabetes treatment. Check out their full paper:

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Categories: Biomedical, Insights

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