ArticleStat (International Statistical Institute)2018
Joint hierarchical Gaussian process model with application to personalized prediction in medical monitoring.
Article in Stat (International Statistical Institute), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Predicting in-hospital indicators from wearable-derived signals for cardiovascular and respiratory disease monitoring: An in silico study.PLOS digital health · 2025Article
- Joint modelling of longitudinal data: a scoping review of methodology and applications for non-time to event data.BMC medical research methodology · 2025Article
- Knowledge-infused Global-Local Data Fusion for Spatial Predictive Modeling in Precision Medicine.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2022Article
- Article
- Risk factor identification in cystic fibrosis by flexible hierarchical joint models.Statistical methods in medical research · 2021Article
- Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods.Diagnostic and prognostic research · 2020Review
- Semiparametric Mixed Models for Medical Monitoring Data: An Overview.Journal of biometrics & biostatistics · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
A two-level Gaussian process (GP) joint model is proposed to improve personalized prediction of medical monitoring data. The proposed model is applied to jointly analyze multiple longitudinal biomedical outcomes, including continuous measurements and binary outcomes, to achieve better prediction in disease progression. At the population level of the hierarchy, two independent GPs are used to capture the nonlinear trends in both the continuous biomedical marker and the binary outcome, respectively; at the individual level, a third GP, which is shared by the longitudinal measurement model and the longitudinal binary model, induces the correlation between these two model components and strengthens information borrowing across individuals. The proposed model is particularly advantageous in personalized prediction. It is applied to the motivating clinical data on cystic fibrosis disease progression, for which lung function measurements and onset of acute respiratory events are monitored jointly throughout each patient's clinical course. The results from both the simulation studies and the cystic fibrosis data application suggest that the inclusion of the shared individual-level GPs under the joint model framework leads to important improvements in personalized disease progression prediction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.