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Future Oncol. 2019 Nov;15(31):3587-3596. doi: 10.2217/fon-2019-0406. Epub 2019 Sep 4.

Automated extraction of treatment patterns from social media posts: an exploratory analysis in renal cell carcinoma.

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Centre for Observational Research & Data Sciences, Bristol-Myers Squibb, Uxbridge UB8 1DH, UK.
Worldwide Health Economics & Outcomes Research, Bristol-Myers Squibb, Uxbridge UB8 1DH, UK.
Real-World Evidence, Evidera, London W6 8DL, UK.


Aim: The use of health-related social media forums by patients is increasing and the size of these forums creates a rich record of patient opinions and experiences, including treatment histories. This study aimed to understand the possibility of extracting treatment patterns in an automated manner for patients with renal cell carcinoma, using natural language processing, rule-based decisions, and machine learning. Patients & methods: Obtained results were compared with those from published observational studies. Results: 42 comparisons across seven therapies, three lines of treatment, and two-time periods were made; 37 of the social media estimates fell within the variation seen across the published studies. Conclusion: This exploratory work shows that estimating treatment patterns from social media is possible and generates results within the variation seen in published studies, although further development and validation of the approach is needed.


natural language processing; oncology; social-media; treatment patterns

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