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

arXiv: https://arxiv.org/abs/1806.01551 · HTML: https://mlsanity.com/paper/1806.01551/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2018.

## Built on by (most cited first, 10 of 10)

- [Personalized Federated Learning with Adaptive Batchnorm for Healthcare](https://mlsanity.com/paper/2112.00734/) (2021)
- [Amortized In-Context Mixed Effect Transformer Models: A Zero-Shot Approach for Pharmacokinetics](https://mlsanity.com/paper/2508.15659/) (2025)
- [Reverse Survival Model (RSM): A Pipeline for Explaining Predictions of Deep Survival Models](https://mlsanity.com/paper/2210.15674/) (2022)
- [Adaptive Shrinkage Estimation For Personalized Deep Kernel Regression In Modeling Brain Trajectories](https://mlsanity.com/paper/2504.08840/) (2025)
- [Uncertainty-Calibrated Prediction of Randomly-Timed Biomarker Trajectories with Conformal Bands](https://mlsanity.com/paper/2511.13911/) (2025)
- [Scalable mixed-domain Gaussian process modeling and model reduction for longitudinal data](https://mlsanity.com/paper/2111.02019/) (2021)
- [A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19](https://mlsanity.com/paper/2203.04383/) (2022)
- [The Climber's Grip -- Personalized Deep Learning Models for Fear and Muscle Activity in Climbing](https://mlsanity.com/paper/2603.26575/) (2026)
- [Bayesian Nonparametric Mixed-Effect ODEs with Gaussian Processes](https://mlsanity.com/paper/2605.13088/) (2026)
- [Adaptive Nystr"om for Gaussian Process Regression](https://mlsanity.com/paper/2607.27427/) (2026)

## Abstract (arXiv)

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).