Errors-in-variables modeling of personalized treatment-response trajectories

Guangyi Zhang, Reza Alizadeh Ashrafi, Anne Juuti, Kirsi Pietiläinen, Pekka Marttinen

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

8 Lataukset (Pure)

Abstrakti

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.
AlkuperäiskieliEnglanti
Artikkeli9072524
Sivut201-208
Sivumäärä8
JulkaisuIEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Vuosikerta25
Numero1
Varhainen verkossa julkaisun päivämäärä20 huhtikuuta 2020
DOI - pysyväislinkit
TilaJulkaistu - tammikuuta 2021
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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