The scientific and medical communities have long recognized human factors influence research results. Indeed, a growing body of literature suggests that even the best-designed medical studies are affected by sources of bias. As medicine embraces an “evidence-based” paradigm in which data drives decisions, it is important to recognize that all evidence comes from human sources. Understanding the researchers behind a paper, and the social and/or meta-networks behind those researchers, is crucial to understand and evaluate research results. To evaluate this properly, it is necessary to employ a set of computational techniques grounded in social network analysis.
In this thesis, I develop and employ the idea of a “medical academic genealogy”, a network of authors linked to a founding department chairman. I demonstrate that identified medical academic genealogies can be correlated with research results, meaning that individuals who train in key genealogies are likely to publish similar results. Additionally, I show that researchers within an academic genealogy are likely to publish in specific journals. As a case study in this phenomena, I examine a controversial neurosurgical issue: the question of extent of surgery for high grade glioma (a type of brain cancer)
To do this, I will pull from an interdisciplinary body of literature, including dynamic network analysis, computer science, information diffusion, neurosurgery, and genealogy studies. The quantitative tools I develop will be important for understanding how individual research papers are interrelated, and can indicate ways in which literature reviews may be unwittingly affected by medical academic genealogy.
Kathleen Carley (Chair)
Rick Carley (Electrical and Computer Engineering)
Clark Chen (University of Minnesota, Department of Neurosurgery)