Forty-two Million Ways to Describe Pain: Topic Modeling of 200,000 PubMed Pain-Related Abstracts Using Natural...

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.

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.

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-embedded model of term definitions available via the "word2vec" technique, the most similar terms to "pain" included "discomfort," "symptom," and "pain-related." For the term "acute," the most similar terms in the word2vec vector space included "nonspecific," "vaso-occlusive," and "subacute"; for the term "chronic," the most similar terms included "persistent," "longstanding," and "long-standing." Topic modeling via Latent Dirichlet analysis identified peak coherence (0.49) at 40 topics. Network analysis of these topic models identified three topics that were outliers from the core cluster, two of which pertained to women's health and obstetrics and were closely connected to one another, yet considered distant from the third outlier pertaining to age. A deep learning-based gated recurrent units abstract generation model successfully synthesized several unique abstracts with varying levels of believability, with special attention and some confusion at lower temperatures to the roles of placebo in randomized controlled trials.

Quantitative NLP models of published abstracts pertaining to pain may point to trends and gaps within pain research communities.