| Literature DB >> 34481080 |
Jonathan Koss1, Astrid Rheinlaender2, Hubert Truebel3, Sabine Bohnet-Joschko4.
Abstract
The incorporation of patients' perspectives into drug discovery and development has become critically important from the viewpoint of accounting for modern-day business dynamics. There is a trend among patients to narrate their disease experiences on social media. The insights gained by analyzing the data pertaining to such social-media posts could be leveraged to support patient-centered drug development. Manual analysis of these data is nearly impossible, but artificial intelligence enables automated and cost-effective processing, also referred as social media mining (SMM). This paper discusses the fundamental SMM methods along with several relevant drug-development use cases.Entities:
Keywords: Innovation; Machine learning; Patient-centered drug development, digital phenotype; Research and development; Social media mining
Mesh:
Year: 2021 PMID: 34481080 DOI: 10.1016/j.drudis.2021.08.012
Source DB: PubMed Journal: Drug Discov Today ISSN: 1359-6446 Impact factor: 7.851