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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

Pain medicine (Malden, Mass.), 2020-11, Vol.21 (11), p.3133-3160 [Peer Reviewed Journal]

The Author(s) 2020. Published by Oxford University Press on behalf of the American Academy of Pain Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com 2020 ;The Author(s) 2020. Published by Oxford University Press on behalf of the American Academy of Pain Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. ;COPYRIGHT 2020 Oxford University Press ;The Author(s) 2020. Published by Oxford University Press on behalf of the American Academy of Pain Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com ;ISSN: 1526-2375 ;EISSN: 1526-4637 ;DOI: 10.1093/pm/pnaa061 ;PMID: 32249306

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  • Title:
    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
  • Author: Tighe, Patrick J ; Sannapaneni, Bharadwaj ; Fillingim, Roger B ; Doyle, Charlie ; Kent, Michael ; Shickel, Ben ; Rashidi, Parisa
  • Subjects: Clinical trials ; Computational linguistics ; Data mining ; Deep Learning ; Endometriosis ; Female ; General & Selected Populations Section ; Humans ; Language ; Language processing ; Machine learning ; Medical research ; Medicine, Experimental ; Natural language interfaces ; Natural Language Processing ; Obstetrics ; Pain ; Placebos ; PubMed
  • Is Part Of: Pain medicine (Malden, Mass.), 2020-11, Vol.21 (11), p.3133-3160
  • Description: 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-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. Conclusions Quantitative NLP models of published abstracts pertaining to pain may point to trends and gaps within pain research communities.
  • Publisher: England: Oxford University Press
  • Language: English
  • Identifier: ISSN: 1526-2375
    EISSN: 1526-4637
    DOI: 10.1093/pm/pnaa061
    PMID: 32249306
  • Source: ProQuest One Psychology
    MEDLINE
    Alma/SFX Local Collection
    ProQuest Central
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