#ChronicPain: Automated Building of a Chronic Pain Cohort from Twitter Using Machine Learning.

ABSTRACT:

Background

Due to the high burden of chronic pain, and the detrimental public health consequences of its treatment with opioids, there is a high-priority need to identify effective alternative therapies. Social media is a potentially valuable resource for knowledge about self-reported therapies by chronic pain sufferers.

Methods

We attempted to (a) verify the presence of large-scale chronic pain-related chatter on Twitter, (b) develop natural language processing and machine learning methods for automatically detecting self-disclosures, (c) collect longitudinal data posted by them, and (d) semiautomatically analyze the types of chronic pain-related information reported by them. We collected data using chronic pain-related hashtags and keywords and manually annotated 4,998 pos

SUBMITTER: Sarker A 

PROVIDER: S-EPMC10852024 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature