ArticleBMC medical informatics and decision making2020
Sparse multi-output Gaussian processes for online medical time series prediction.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Data-Driven Insights into Labor Progression with Gaussian Processes.Bioengineering (Basel, Switzerland) · 2024Article
- Predictive modeling of biomedical temporal data in healthcare applications: review and future directions.Frontiers in physiology · 2024Review
- Online model selection by learning how compositional kernels evolve.Transactions on machine learning research · 2023Article
- Oxygenation thresholds for invasive ventilation in hypoxemic respiratory failure: a target trial emulation in two cohorts.Critical care (London, England) · 2023Article
- Multi-outputs Gaussian process for predicting Burkina Faso COVID-19 spread using correlations from the weather parameters.Infectious Disease Modelling · 2022Review
- Hierarchical Gaussian Processes and Mixtures of Experts to Model COVID-19 Patient Trajectories.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2022Article
- A survival analysis based volatility and sparsity modeling network for student dropout prediction.PloS one · 2022Article
- Study on the Prediction Method of Long-term Benign and Malignant Pulmonary Lesions Based on LSTM.Frontiers in bioengineering and biotechnology · 2022Article
- Longitudinal K-means approaches to clustering and analyzing EHR opioid use trajectories for clinical subtypes.Journal of biomedical informatics · 2021Review
- An Optimal Policy for Patient Laboratory Tests in Intensive Care Units.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundFor real-time monitoring of hospital patients, high-quality inference of patients' health status using all information available from clinical covariates and lab test results is essential to enable successful medical interventions and improve patient outcomes. Developing a computational framework that can learn from observational large-scale electronic health records (EHRs) and make accurate real-time predictions is a critical step. In this work, we develop and explore a Bayesian nonparametric model based on multi-output Gaussian process (GP) regression for hospital patient monitoring.
methodsWe propose MedGP, a statistical framework that incorporates 24 clinical covariates and supports a rich reference data set from which relationships between observed covariates may be inferred and exploited for high-quality inference of patient state over time. To do this, we develop a highly structured sparse GP kernel to enable tractable computation over tens of thousands of time points while estimating correlations among clinical covariates, patients, and periodicity in patient observations. MedGP has a number of benefits over current methods, including (i) not requiring an alignment of the time series data, (ii) quantifying confidence regions in the predictions, (iii) exploiting a vast and rich database of patients, and (iv) inferring interpretable relationships among clinical covariates.
resultsWe evaluate and compare results from MedGP on the task of online prediction for three patient subgroups from two medical data sets across 8,043 patients. We find MedGP improves online prediction over baseline and state-of-the-art methods for nearly all covariates across different disease subgroups and hospitals.
conclusionsThe MedGP framework is robust and efficient in estimating the temporal dependencies from sparse and irregularly sampled medical time series data for online prediction. The publicly available code is at https://github.com/bee-hive/MedGP .
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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.