# Imputation of Clinical Covariates in Time Series

arXiv: https://arxiv.org/abs/1812.00418 · HTML: https://mlsanity.com/paper/1812.00418/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2018.

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

- [GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing](https://mlsanity.com/paper/2405.11333/) (2024)
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- [Clairvoyance: A Pipeline Toolkit for Medical Time Series](https://mlsanity.com/paper/2310.18688/) (2023)
- [Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness](https://mlsanity.com/paper/2208.06648/) (2022)
- [Algorithmic Insurance](https://mlsanity.com/paper/2106.00839/) (2021)
- [Some of the variables, some of the parameters, some of the times, with some physics known: Identification with partial information](https://mlsanity.com/paper/2304.14214/) (2023)
- [Compound Density Networks for Risk Prediction using Electronic Health Records](https://mlsanity.com/paper/2208.01320/) (2022)
- [Simple Imputation Rules for Prediction with Missing Data: Contrasting Theoretical Guarantees with Empirical Performance](https://mlsanity.com/paper/2104.03158/) (2021)
- [Deep sub-ensembles meets quantile regression: uncertainty-aware imputation for time series](https://mlsanity.com/paper/2312.01294/) (2023)
- [Towards Optimal Valve Prescription for Transcatheter Aortic Valve Replacement (TAVR) Surgery: A Machine Learning Approach](https://mlsanity.com/paper/2512.09198/) (2025)

## Abstract (arXiv)

Missing data is a common problem in real-world settings and particularly relevant in healthcare applications where researchers use Electronic Health Records (EHR) and results of observational studies to apply analytics methods. This issue becomes even more prominent for longitudinal data sets, where multiple instances of the same individual correspond to different observations in time. Standard imputation methods do not take into account patient specific information incorporated in multivariate panel data. We introduce the novel imputation algorithm MedImpute that addresses this problem, extending the flexible framework of OptImpute suggested by Bertsimas et al. (2018). Our algorithm provides imputations for data sets with missing continuous and categorical features, and we present the formulation and implement scalable first-order methods for a K-NN model. We test the performance of our algorithm on longitudinal data from the Framingham Heart Study when data are missing completely at random (MCAR). We demonstrate that MedImpute leads to significant improvements in both imputation accuracy and downstream model AUC compared to state-of-the-art methods.