Forty-two Million Ways to Describe Pain: Topic Modeling of 200,000 PubMed Pain-Related Abstracts Using Natural Language Processing and Deep Learning-Based Text Generation.

ABSTRACT:

Objective

Recent efforts to update the definitions and taxonomic structure of concepts related to pain have revealed opportunities to better quantify topics of existing pain research subject areas.

Methods

Here, we apply basic natural language processing (NLP) analyses on a corpus of >200,000 abstracts published on PubMed under the medical subject heading (MeSH) of "pain" to quantify the topics, content, and themes on pain-related research dating back to the 1940s.

Results

The most common stemmed terms included "pain" (601,122 occurrences), "patient" (508,064 occurrences), and "studi-" (208,839 occurrences). Contrarily, terms with the highest term frequency-inverse document frequency included "tmd" (6.21), "qol" (6.01), and "endometriosis" (5.94). Using the vector-emb

SUBMITTER: Tighe PJ 

PROVIDER: S-EPMC7685694 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature