# Modeling Missing Data in Clinical Time Series with RNNs

arXiv: https://arxiv.org/abs/1606.04130 · HTML: https://mlsanity.com/paper/1606.04130/

## Key facts

- Cited by 80 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2016.

## Built on by (most cited first, 30 of 80)

- [Multi-Time Attention Networks for Irregularly Sampled Time Series](https://mlsanity.com/paper/2101.10318/) (2021)
- [Modeling Irregular Time Series with Continuous Recurrent Units](https://mlsanity.com/paper/2111.11344/) (2021)
- [Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series](https://mlsanity.com/paper/2107.14293/) (2021)
- [Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks](https://mlsanity.com/paper/2108.00298/) (2021)
- [Time Series as Images: Vision Transformer for Irregularly Sampled Time Series](https://mlsanity.com/paper/2303.12799/) (2023)
- [Time Series Prediction using Deep Learning Methods in Healthcare](https://mlsanity.com/paper/2108.13461/) (2021)
- [ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling](https://mlsanity.com/paper/2402.10635/) (2024)
- [Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction](https://mlsanity.com/paper/2303.16005/) (2023)
- [CUTS+: High-dimensional Causal Discovery from Irregular Time-series](https://mlsanity.com/paper/2305.05890/) (2023)
- [Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling](https://mlsanity.com/paper/2210.12156/) (2022)
- [Graph Deep Learning for Time Series Forecasting](https://mlsanity.com/paper/2310.15978/) (2023)
- [NRTSI: Non-Recurrent Time Series Imputation](https://mlsanity.com/paper/2102.03340/) (2021)
- [Trajectory Flow Matching with Applications to Clinical Time Series Modeling](https://mlsanity.com/paper/2410.21154/) (2024)
- [MIRA: Medical Time Series Foundation Model for Real-World Health Data](https://mlsanity.com/paper/2506.07584/) (2025)
- [Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network](https://mlsanity.com/paper/2308.16818/) (2023)
- [Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Series](https://mlsanity.com/paper/2506.10412/) (2025)
- [Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions](https://mlsanity.com/paper/2507.00191/) (2025)
- [Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis](https://mlsanity.com/paper/2203.14469/) (2022)
- [Stop&Hop: Early Classification of Irregular Time Series](https://mlsanity.com/paper/2208.09795/) (2022)
- [Unleashing The Power of Pre-Trained Language Models for Irregularly Sampled Time Series](https://mlsanity.com/paper/2408.08328/) (2024)
- [Gradient Importance Learning for Incomplete Observations](https://mlsanity.com/paper/2107.01983/) (2021)
- [MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot Communication](https://mlsanity.com/paper/2203.02877/) (2022)
- [A Two-Block RNN-based Trajectory Prediction from Incomplete Trajectory](https://mlsanity.com/paper/2203.07098/) (2022)
- [Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness](https://mlsanity.com/paper/2208.06648/) (2022)
- [DeepJoint: Robust Survival Modelling Under Clinical Presence Shift](https://mlsanity.com/paper/2205.13481/) (2022)
- [HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare](https://mlsanity.com/paper/2302.09212/) (2023)
- [Learning under Temporal Label Noise](https://mlsanity.com/paper/2402.04398/) (2024)
- [EMIT- Event-Based Masked Auto Encoding for Irregular Time Series](https://mlsanity.com/paper/2409.16554/) (2024)
- [Reconstructing Missing EHRs Using Time-Aware Within- and Cross-Visit Information for Septic Shock Early Prediction](https://mlsanity.com/paper/2203.08245/) (2022)
- [DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Series](https://mlsanity.com/paper/2208.11374/) (2022)

## Abstract (arXiv)

We demonstrate a simple strategy to cope with missing data in sequential inputs, addressing the task of multilabel classification of diagnoses given clinical time series. Collected from the pediatric intensive care unit (PICU) at Children's Hospital Los Angeles, our data consists of multivariate time series of observations. The measurements are irregularly spaced, leading to missingness patterns in temporally discretized sequences. While these artifacts are typically handled by imputation, we achieve superior predictive performance by treating the artifacts as features. Unlike linear models, recurrent neural networks can realize this improvement using only simple binary indicators of missingness. For linear models, we show an alternative strategy to capture this signal. Training models on missingness patterns only, we show that for some diseases, what tests are run can be as predictive as the results themselves.