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

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

Abstract

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.

Keywords: natural language processing; oncology; social-media; treatment patterns.

MeSH terms

  • Algorithms
  • Antineoplastic Agents / administration & dosage
  • Antineoplastic Agents / adverse effects
  • Antineoplastic Agents / therapeutic use
  • Antineoplastic Combined Chemotherapy Protocols / adverse effects
  • Antineoplastic Combined Chemotherapy Protocols / therapeutic use
  • Carcinoma, Renal Cell / epidemiology*
  • Carcinoma, Renal Cell / therapy
  • Data Interpretation, Statistical
  • Data Mining*
  • Humans
  • Kidney Neoplasms / epidemiology*
  • Kidney Neoplasms / therapy
  • Machine Learning
  • Social Media*
  • Web Browser

Substances

  • Antineoplastic Agents