Abstract
Estimating the impact of a treatment on a given response is needed in many biomedical applications. However, methodology is lacking for the case when the response is a continuous temporal curve, treatment covariates suffer extensively from measurement error, and even the exact timing of the treatments is unknown. We introduce a novel method for this challenging scenario. We model personalized treatment-response curves as a combination of parametric response functions, hierarchically sharing information across individuals, and a sparse Gaussian process for the baseline trend. Importantly, our model accounts for errors not only in treatment covariates, but also in treatment timings, a problem arising in practice for example when data on treatments are based on user self-reporting. We validate our model with simulated and real patient data, and show that in a challenging application of estimating the impact of diet on continuous blood glucose measurements, accounting for measurement error significantly improves estimation and prediction accuracy.
Highlights
I NCREASING popularity of electronic health records (EHRs) and smart healthcare services has led to Manuscript received October 28, 2019; revised March 2, 2020; accepted April 6, 2020
Throughout, we present the model in generic terms, and outline the specific model used in Section IV-B to estimate the impact of diet on continuous blood glucose measurements
As the first simple experiment we study the identifiability of the EIV model when there is measurement error in covariates
Summary
I NCREASING popularity of electronic health records (EHRs) and smart healthcare services has led to Manuscript received October 28, 2019; revised March 2, 2020; accepted April 6, 2020. Date of publication April 20, 2020; date of current version January 5, 2021. This article has supplementary downloadable material available at https://ieeexplore.ieee.org, provided by the authors. Color versions of one or more of the figures in this paper are available online at https://ieeexplore.ieee.org
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.