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Activity Number: 696
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #321247 View Presentation
Title: Longitudinal Study of NHDS Data Using NFCA for Aging Population
Author(s): Junheng Ma* and Jiayang Sun and Neal Dawson
Companies: Case Western Reserve University and Case Western Reserve University and Case Western Reserve University/MetroHealth Medical Center
Keywords: ICD-9 code ; longitudinal study ; risk factor ; diagnostic network ; disparity ; EHR

Identifying risk factors and building biological networks are critically important not only in clinical research but also in disease prevention and early diagnosis. In this longitudinal study, we analyzed NHDS data using our new machine learning technique: numerical Formal Concept Analysis(nFCA) for aging population to identify important risk factors and construct diagnostic networks as well as study longitudinal changes for different cohorts. nFCA distinguishes from existing techniques in combining the merit of statistical and computer science techniques to reveal simultaneously the clusters and inherent connection by a hierarchical clustering network(H-graph) and an inherent relational graph(I-graph). Our study shows that circulatory system diseases remain the most dominant diseases throughout for the general, white and most of the years of the Africa American populations although the disease patterns changed gradually and significantly from the earlier years. The disease patterns for the white and general populations are similar except some minor disparities while the disease patterns for the African American population show noticeable disparities from the general population.

Authors who are presenting talks have a * after their name.

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