Understanding the research landscape of major depressive disorder via literature mining: an entity-level analysis of PubMed data from 1948 to 2017. Academic Article uri icon



  • Objective: To analyze literature-based data from PubMed to identify diseases and medications that have frequently been studied with major depressive disorder (MDD). Materials and methods: Abstracts of 23 799 research articles about MDD that have been published since 1948 till 2017 were analyzed using data and text mining approaches. Methods such as information extraction, frequent pattern mining, regression, and burst detection were used to explore diseases and medications that have been associated with MDD. Results: In addition to many mental disorders and antidepressants, we identified several nonmental health diseases and nonpsychotropic medications that have frequently been studied with MDD. Our results suggest that: (1) MDD has been studied with disorders such as Pain, Diabetes Mellitus, Wounds and Injuries, Hypertension, and Cardiovascular Diseases; (2) medications such as Hydrocortisone, Dexamethasone, Ketamine, and Lithium have been studied in terms of their side effects and off-label uses; (3) the relationships between nonmental disorders and MDD have gained increased attention from the scientific community; and (4) the bursts of Diabetes Mellitus and Cardiovascular Diseases explain the psychiatric and/or depression screening recommended by authoritative associations during the periods of the bursts. Discussion and conclusion: This study summarized and presented an overview of the previous MDD research in terms of diseases and medications that are highly relevant to MDD. The reported results can potentially facilitate hypothesis generation for future studies. The approaches proposed in the study can be used to better understand the progress and advance of the field.

publication date

  • April 3, 2018


PubMed Central ID

  • PMC6951824

Digital Object Identifier (DOI)

  • 10.1093/jamiaopen/ooy001

PubMed ID

  • 31984323

Additional Document Info


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