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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
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.
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.
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