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Gaussian process regression and classification using International Classification of Disease codes as covariates
Journal article   Open access   Peer reviewed

Gaussian process regression and classification using International Classification of Disease codes as covariates

Sanvesh Srivastava, Zongyi Xu, Yunyi Li, W Nick Street and Stephanie Gilbertson-White
Stat, Vol.12(1), e618
2023
DOI: 10.1002/sta4.618
Appears in  UI Libraries Support Open Access
url
https://doi.org/10.1002/sta4.618View
Published (Version of record) Open Access

Abstract

In electronic health records (EHRs) data analysis, nonparametric regression and classification using International Classification of Disease (ICD) codes as covariates remain understudied. Automated methods have been developed over the years for predicting biomedical responses using EHRs, but relatively less attention has been paid to developing patient similarity measures that use ICD codes and chronic conditions, where a chronic condition is defined as a set of ICD codes. We address this problem by first developing a string kernel function for measuring the similarity between a pair of primary chronic conditions, represented as subsets of ICD codes. Second, we extend this similarity measure to a family of covariance functions on subsets of chronic conditions. This family is used in developing Gaussian process (GP) priors for Bayesian nonparametric regression and classification using diagnoses and other demographic information as covariates. Markov chain Monte Carlo (MCMC) algorithms are used for posterior inference and predictions. The proposed methods are tuning free, so they are ideal for automated prediction of biomedical responses depending on chronic conditions. We evaluate the practical performance of our method on EHR data collected from 1660 patients at the University of Iowa Hospitals and Clinics (UIHC) with six different primary cancer sites. Our method provides better sensitivity and specificity than its competitors in classifying different primary cancer sites and estimates the marginal associations between chronic conditions and primary cancer sites.
 Bayesian learning Gaussian process Markov chain Monte Carlo nonparametric regression and classification string kernels UIOWA OA Agreement

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