A0870
Title: Analysis of disease prevalence: A clustering perspective that incorporates prior information
Authors: Chenjin Ma - Beijing University of Technology (China) [presenting]
Abstract: The analysis of disease prevalence is of critical importance in biomedical research. The collective analysis of multiple diseases, significantly different from individual disease analysis, can provide valuable additional insights. A critical limitation of the existing analysis is that there is a lack of attention to prior information, which has been accumulated through many studies and can be valuable, especially when there are a large number of diseases, but the number of prevalence values for a specific disease is limited. The functional clustering analysis of disease prevalence trends is conducted. A novel approach based on the penalized fusion technique is developed to incorporate prior information mined from published articles. It is innovatively designed to take into account that such information may not be fully relevant or correct. Rigorous statistical and computational investigations are conducted. In the analysis of data from Taiwan NHIRD (National Health Insurance Research Database), new and interesting findings that differ from the existing ones are made.
