Insights into Foundation Models for Electronic Health Records: Representation Dynamics and Transferability
The paper "Foundation Models for Electronic Health Records: Representation Dynamics and Transferability" explores the use of foundation models (FMs) trained on electronic health records (EHRs) for prognostic tasks. This paper delves deeply into the adaptability of these models across various health systems, focusing on the transition of models trained with MIMIC-IV data to the data of the University of Chicago Medical Center (UCMC).
Key Objectives and Methods
The primary aim of the paper is to evaluate the transferability of FMs trained on MIMIC-IV to other institutional EHR datasets, specifically those at UCMC, recognizing the challenges posed by distribution shifts. The paper assesses the FMs' ability to pinpoint outlier patients and scrutinizes patient trajectories in the latent representation space, correlating them with future clinical outcomes. The paper encompasses the use of LLM architectures to handle tokenized EHR sequences and involves extracting latent representations to facilitate various clinical predictive tasks.
The researchers employ logistic regression for representation-based classifiers and implement Isolation Forest for outlier detection in the data sourced from the MIMIC set. Several predictive outcomes, such as inpatient mortality, long length of stay, ICU admission, and invasive mechanical ventilation (IMV) events, are central to this evaluation.
Results and Analysis
The paper finds that the performance of representation-based classifiers is adequate within the MIMIC environment but degrades significantly when transferred to UCMC, particularly for predicting ICU admissions and IMV events. There is, however, a more robust cross-site generalization for inpatient mortality prediction. Fine-tuning shows substantial benefits, enhancing model performance especially when transferring to the UCMC dataset. Models benefit further from local fine-tuning using a small percentage of UCMC-specific data.
An analysis of the representation dynamics reveals consistent patterns that can predict adverse outcomes. The trajectory length, maximum jump in representation space, and anomaly scores are reliable predictors of patient deterioration, highlighting the potential of these metrics in early risk stratification.
Implications and Future Directions
The paper's findings underscore the challenges and necessities of deploying foundation models in healthcare environments where EHRs vary significantly between institutions. While FMs trained on one dataset may not directly apply to another, fine-tuning on local data—even in limited quantities—can mitigate performance declines. This adaptability of FMs suggests that with strategic local fine-tuning, these models can serve across various institutional settings, enhancing clinical applications like patient risk assessment and resource allocation.
For future work, there is potential in exploring more sophisticated methods for unsupervised anomaly detection and extending the temporal horizon of the data inputs. Additionally, a more expansive application of these models across diverse health systems could provide further insights into the scalability and robustness of FMs in the highly heterogeneous domain of healthcare data. This paper not only informs the practical deployment of AI in clinical settings but also invites further exploration into the nuances of FM adaptation in varying healthcare environments.