# Generalizability of predictive models for intensive care unit patients

arXiv: https://arxiv.org/abs/1812.02275 · HTML: https://mlsanity.com/paper/1812.02275/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2018.

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

- [Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning](https://mlsanity.com/paper/2108.08435/) (2021)
- ["Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts](https://mlsanity.com/paper/2210.10769/) (2022)
- [Pre-registration for Predictive Modeling](https://mlsanity.com/paper/2311.18807/) (2023)
- [A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs](https://mlsanity.com/paper/2504.14657/) (2025)
- [Structured Semantics from Unstructured Notes: Language Model Approaches to EHR-Based Decision Support](https://mlsanity.com/paper/2506.06340/) (2025)
- [Looking for Out-of-Distribution Environments in Multi-center Critical Care Data](https://mlsanity.com/paper/2205.13398/) (2022)
- [Prediction of Survival Outcomes under Clinical Presence Shift: A Joint Neural Network Architecture](https://mlsanity.com/paper/2508.05472/) (2025)
- [Towards Robust Off-Policy Evaluation via Human Inputs](https://mlsanity.com/paper/2209.08682/) (2022)
- [Anti-Matthew FL: Bridging the Performance Gap in Federated Learning to Counteract the Matthew Effect](https://mlsanity.com/paper/2309.16338/) (2023)

## Abstract (arXiv)

A large volume of research has considered the creation of predictive models for clinical data; however, much existing literature reports results using only a single source of data. In this work, we evaluate the performance of models trained on the publicly-available eICU Collaborative Research Database. We show that cross-validation using many distinct centers provides a reasonable estimate of model performance in new centers. We further show that a single model trained across centers transfers well to distinct hospitals, even compared to a model retrained using hospital-specific data. Our results motivate the use of multi-center datasets for model development and highlight the need for data sharing among hospitals to maximize model performance.