Browsing by Author "Zhang, Guangyi"
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- Errors-in-variables modeling of personalized treatment-response trajectories
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2021-01) Zhang, Guangyi; Alizadeh Ashrafi, Reza; Juuti, Anne; Pietiläinen, Kirsi; Marttinen, PekkaEstimating 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. - Maximizing diversity over clustered data*
A4 Artikkeli konferenssijulkaisussa(2020) Zhang, Guangyi; Gionis, AristidesMaximum diversity aims at selecting a diverse set of high-quality objects from a collection, which is a fundamental problem and has a wide range of applications, e.g., in Web search. Diversity under a uniform or partition matroid constraint naturally describes useful cardinality or budget requirements, and admits simple approximation algorithms [5]. When applied to clustered data, however, popular algorithms such as picking objects iteratively and performing local search lose their approximation guarantees towards maximum intra-cluster diversity because they fail to optimize the objective in a global manner. We propose an algorithm that greedily adds a pair of objects instead of a singleton, and which attains a constant approximation factor over clustered data. We further extend the algorithm to the case of monotone and submodular quality function, and under a partition matroid constraint. We also devise a technique to make our algorithm scalable, and on the way we obtain a modification that gives better solutions in practice while maintaining the approximation guarantee in theory. Our algorithm achieves excellent performance, compared to strong baselines in a mix of synthetic and real-world datasets. - Personalized Treatment-Response Trajectories: Errors-in-variables, Interpretability, and Causality
Perustieteiden korkeakoulu | Master's thesis(2019-06-17) Zhang, GuangyiOne fundamental problem in many applications is to estimate treatment-response trajectories given multidimensional treatment variables. However, in reality, the estimation suffers severely from measurement error both in treatment timing and covariates, for example when the treatment data are self-reported by users. We introduce a novel data-driven method to tackle this challenging problem, which models personalized treatment-response trajectories as a sum of a parametric response function, based on restored true treatment timing and covariates and sharing information across individuals under a hierarchical structure, and a counterfactual trend fitted by a sparse Gaussian Process. In a real-life dataset where the impact of diet on continuous blood glucose is estimated, our model achieves a superior performance in estimation accuracy and prediction.