Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Aug 17, 2020
Date Accepted: Dec 9, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Exploring the Interdisciplinary Nature of Precision Medicine with Social Network Analysis and Co-Occurrence Analysis
ABSTRACT
Background:
From the perspective of medical informatics, interdisciplinary research is an important feature of PM. However, a detailed and accurate assessment of the cross-disciplinary status of PM is still lacking.
Objective:
The aim of this study is to present the nature of interdisciplinary collaboration in precision medicine (PM) based on co-occurrences and social network analysis.
Methods:
PM studies published between 2010 and 2019 were collected from the Web of Science database. We analyzed interdisciplinarity with descriptive statistics, co-occurrence and social network analysis. An evolutionary graph and strategic diagram were created to clarify the development of streams and trends in disciplinary communities.
Results:
The results indicate that many disciplines are involved in PM research and cover a wide range. However, the disciplinary distribution is unbalanced. Current cross-disciplinary collaboration in PM mainly focuses on clinical application and technology-associated disciplines. The characteristics of the disciplinary collaboration network are as follows: (1) disciplinary cooperation in PM is not mature or centralized; (2) the leading disciplines are absent; (3) the pattern of disciplinary cooperation is mostly indirect rather than direct. There are seven interdisciplinary communities in the PM collaboration network; however, their positions in the network differ. Community 4, with disciplines such as genetics & heredity in the core position, is the most central and cooperative discipline in the interdisciplinary network. This indicates that Community 4 represents a relatively mature direction in interdisciplinary cooperation in PM. Finally, according to the evolution graph, we clearly present the development streams of disciplinary collaborations in PM. We describe the scale and the time frame for development trends and distributions in detail. Importantly, we accurately estimate the developmental trend of PM based on evolution graphs, such as biological big data processing, molecular imaging and widespread clinical applications.
Conclusions:
This study can help researchers, clinicians, and policymakers comprehensively understand the overall network of interdisciplinary cooperation in PM. More importantly, we quantitatively and precisely present the history of interdisciplinary cooperation and accurately predict the developing trends of interdisciplinary cooperation in PM.
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