Document Type
Article
Publication Date
6-13-2014
Digital Object Identifier (DOI)
http://dx.doi.org/10.1371/journal.pone.0091095
Abstract
Objective: To identify the risk-predictive baseline profile patterns of demographic, genetic, immunologic, and metabolic markers and synthesize these patterns for risk prediction.
Research Design and Methods: RuleFit is used to identify the risk-predictive baseline profile patterns of demographic, immunologic, and metabolic markers, using 356 subjects who were randomized into the control arm of the prospective Diabetes Prevention Trial-Type 1 (DPT-1) study. A novel latent trait model is developed to synthesize these baseline profile patterns for disease risk prediction. The primary outcome was Type 1 Diabetes (T1D) onset.
Results: We identified ten baseline profile patterns that were significantly predictive to the disease onset. Using these ten baseline profile patterns, a risk prediction model was built based on the latent trait model, which produced superior prediction performance over existing risk score models for T1D.
Conclusion: Our results demonstrated that the underlying disease progression process of T1D can be detected through some risk-predictive patterns of demographic, immunologic, and metabolic markers. A synthesis of these patterns provided accurate prediction of disease onset, leading to more cost-effective design of prevention trials of T1D in the future.
Rights Information
This work is licensed under a Creative Commons Attribution 3.0 License.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
PLoS ONE, v. 9, issue 9, art. e109514
Scholar Commons Citation
Lin, Ying; Qian, Xiaoning; Krischer, Jeffrey; Vehik, Kendra; Lee, Hye-Seung; and Huang, Shuai, "A Rule-Based Prognostic Model for Type 1 Diabetes by Identifying and Synthesizing Baseline Profile Patterns" (2014). Industrial and Management Systems Engineering Faculty Publications. 1.
https://digitalcommons.usf.edu/egs_facpub/1
