# PRISM: Mitigating EHR Data Sparsity via Learning from Missing Feature Calibrated Prototype Patient Representations

arXiv: https://arxiv.org/abs/2309.04160 · HTML: https://mlsanity.com/paper/2309.04160/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 8 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism](https://mlsanity.com/paper/1608.05745/) (2016)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesis](https://mlsanity.com/paper/2002.05000/) (2020)
- [M3Care: Learning with Missing Modalities in Multimodal Healthcare Data](https://mlsanity.com/paper/2210.17292/) (2022)
- [ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context](https://mlsanity.com/paper/1911.12216/) (2019)
- [AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration](https://mlsanity.com/paper/1911.12205/) (2019)
- [Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review](https://mlsanity.com/paper/2010.02809/) (2020)
- [Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques](https://mlsanity.com/paper/2210.08258/) (2022)
- [A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care](https://mlsanity.com/paper/2209.07805/) (2022)
- [M3Fair: Mitigating Bias in Healthcare Data through Multi-Level and Multi-Sensitive-Attribute Reweighting Method](https://mlsanity.com/paper/2306.04118/) (2023)

## Built on by (most cited first, 9 of 9)

- [REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models](https://mlsanity.com/paper/2402.07016/) (2024)
- [ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent Collaboration](https://mlsanity.com/paper/2410.02551/) (2024)
- [Diffmv: A Unified Diffusion Framework for Healthcare Predictions with Random Missing Views and View Laziness](https://mlsanity.com/paper/2505.11802/) (2025)
- [MORE-CLEAR: Multimodal Offline Reinforcement learning for Clinical notes Leveraged Enhanced State Representation](https://mlsanity.com/paper/2508.07681/) (2025)
- [ProtoEHR: Hierarchical Prototype Learning for EHR-based Healthcare Predictions](https://mlsanity.com/paper/2508.18313/) (2025)
- [TAMER: A Test-Time Adaptive MoE-Driven Framework for EHR Representation Learning](https://mlsanity.com/paper/2501.05661/) (2025)
- [Augmenting Clinical Decision-Making with an Interactive and Interpretable AI Copilot: A Real-World User Study with Clinicians in Nephrology and Obstetrics](https://mlsanity.com/paper/2602.00726/) (2026)
- [ReToP: Learning to Rewrite Electronic Health Records for Clinical Prediction](https://mlsanity.com/paper/2601.19286/) (2026)
- [Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented Generation](https://mlsanity.com/paper/2607.18270/) (2026)

## Abstract (arXiv)

Electronic Health Records (EHRs) contain a wealth of patient data; however, the sparsity of EHRs data often presents significant challenges for predictive modeling. Conventional imputation methods inadequately distinguish between real and imputed data, leading to potential inaccuracies of patient representations. To address these issues, we introduce PRISM, a framework that indirectly imputes data by leveraging prototype representations of similar patients, thus ensuring compact representations that preserve patient information. PRISM also includes a feature confidence learner module, which evaluates the reliability of each feature considering missing statuses. Additionally, PRISM introduces a new patient similarity metric that accounts for feature confidence, avoiding over-reliance on imprecise imputed values. Our extensive experiments on the MIMIC-III, MIMIC-IV, PhysioNet Challenge 2012, eICU datasets demonstrate PRISM's superior performance in predicting in-hospital mortality and 30-day readmission tasks, showcasing its effectiveness in handling EHR data sparsity. For the sake of reproducibility and further research, we have publicly released the code at https://github.com/yhzhu99/PRISM.