Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare

2018/06/05 by Ingyo Chung, Chung, Ingyo, Saehoon Kim +9 · 3 citations
Computer Science · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare #Time Series Analysis and Forecasting

paper · pdf · doi:10.48550/arxiv.1806.01551

openalex publication_date 2018/06/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment, and prevention. Our proposed framework targets at making personalized and reliable predictions from time-series data, such as Electronic Health Records (EHR), by modeling two complementary components: i) a shared component that captures global trend across diverse patients and ii) a patient-specific component that models idiosyncratic variability for each patient. To this end, we propose a composite model of a deep neural network to learn complex global trends from the large number of patients, and Gaussian Processes (GP) to probabilistically model individual time-series given relatively small number of visits per patient. We evaluate our model on diverse and heterogeneous tasks from EHR datasets and show practical advantages over standard time-series deep models such as pure Recurrent Neural Network (RNN).

Citations

• Scalable Variational Gaussian Process Classification
• Adaptive Mixtures of Local Experts
• PhysioBank, PhysioToolkit, and PhysioNet
• Personalized Risk Scoring for Critical Care Patients using Mixtures of Gaussian Process Experts
• Modeling Missing Data in Clinical Time Series with RNNs
• Meta-SGD: Learning to Learn Quickly for Few-Shot Learning
• An Improved Multi-Output Gaussian Process RNN with Real-Time Validation\n for Early Sepsis Detection
• RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism
• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Cited by

• Uncertainty-Calibrated Prediction of Randomly-Timed Biomarker Trajectories with Conformal Bands
• Adaptive Nyström for Gaussian Process Regression
• Amortized In-Context Mixed Effect Transformer Models: A Zero-Shot Approach for Pharmacokinetics

Related

• Sparse Multi-Output Gaussian Processes for Medical Time Series Prediction
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• Time Series Prediction using Deep Learning Methods in Healthcare
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• Scalable and accurate deep learning with electronic health records