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Disentangling the evolution of MEDLINE bibliographic database: A complex network perspective.

Andrej Kastrin1, Dimitar Hristovski1

  • 1Institute of Biostatistics and Medical Informatics, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, SI-1000 Ljubljana, Slovenia.

Journal of Biomedical Informatics
|December 12, 2018
PubMed
Summary

Scientific knowledge evolution in life sciences was analyzed using MEDLINE data. New discoveries often link existing concepts, forming and dissolving research communities over time.

Keywords:
Bibliographic databasesComplex networksMEDLINENetwork evolutionScience of science

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Area of Science:

  • Life Sciences
  • Bibliometrics
  • Network Science

Background:

  • Scientific knowledge is a complex system with poorly understood dynamics.
  • Analyzing the expansion of scientific knowledge aids understanding of science's evolution.
  • The MEDLINE database offers a rich source for studying scientific knowledge growth.

Purpose of the Study:

  • To analyze the dynamic properties and growth principles of the MEDLINE bibliographic database.
  • To represent the scientific evolution of life sciences as a network of co-occurring MeSH descriptors.
  • To track the temporal evolution of scientific knowledge within the MEDLINE network.

Main Methods:

  • Network analysis methodology applied to the MEDLINE database (1966-2014).
  • Representing MEDLINE citations as a complex system of nodes (knowledge concepts) and edges (relations).
  • Statistical evaluation based on over 25 million citations, analyzing node and community evolution.

Main Results:

  • The network's degree distribution follows a stretched exponential pattern, inhibiting large hubs.
  • New MeSH terms do not always lead to new connections; most arise from existing descriptors.
  • 142 evolving communities were identified, demonstrating a life cycle of emergence, existence, and dissolution.

Conclusions:

  • Scientific discovery often arises from connecting previously disconnected knowledge areas (structural holes).
  • The evolution of knowledge in MEDLINE is intrinsically linked to the network's structural and temporal characteristics.
  • A web-based application was developed to help characterize and understand these evolving research communities.