Year: 2017
Type: dissertation
Abstract: High dimensionality in Big Data can be modeled using network approach. The traditional networks (e.g. online social network) are explicit and easily observed. However, there are certain networks that are implicit and exist by virtue of some underlying collective behavior. Our focus is on these implicit networks, which can be inferred from the secondary data using statistical modeling. An example of such a network is a comorbidity network. In a comorbidity network, diseases form connections based on their co-occurrences in patients. We use data on the health history of 24.7 million patients recorded in US hospitals (2000-2016) to infer comorbidity networks. Since most statistical models depend upon sample size, it is important to study how sample size affects the structure of an implicit network. We study the impact of sample size on comorbidity networks developed using Pearson's Correlation Coefficient (PCC) and Salton Cosine Index (SCI). We present a comparative analysis and show that a network developed using SCI is robust to sample size as compared to the PCC.
Author: Pankush Kalgotra
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funder: Oklahoma State University
OpenAlex ID: W2949955227