Overview
FedEHR-Agents is a proposed experience-centric federated agentic optimization framework designed for automated Electronic Health Record (EHR) modeling. This framework addresses limitations of existing federated learning (FL) approaches, which primarily focus on model parameters, by instead federating the 'richer modeling experience' accumulated by autonomous clinical agents.
The core mechanism involves each hospital deploying an autonomous clinical EHR agent. These agents execute data preprocessing and model development tasks locally. Concurrently, they refine their local clinical modeling experience through historical memory, task-specific evaluation, and a TextGrad-based prompt refinement process.
A federated server component is responsible for evidence-guided experience aggregation. This aggregation integrates reliable and complementary modeling experience from various heterogeneous hospitals. Subsequently, the aggregated experience is distilled into global meta-prompts, which are then used for further local refinement by the individual hospital agents.
Research Context
Recent advancements in large language models (LLMs) have enabled autonomous clinical agents to manage increasingly complex EHR modeling workflows. However, agents operating at individual hospitals encounter limitations due to institution-specific data and unique modeling environments. Direct collaboration across hospitals is restricted by the sensitive nature of patient-level EHR data.
Federated learning (FL) offers a privacy-preserving foundation for collaboration in such scenarios. Nevertheless, current FL methodologies are predominantly model-centric. They typically restrict federation to prediction models or their updates, thereby overlooking the comprehensive modeling experience that autonomous agents accumulate during their operation. This gap motivates the development of FedEHR-Agents to broaden the scope of federated collaboration beyond mere model parameters.
Approach
The FedEHR-Agents framework introduces an experience-centric approach to federated agentic optimization. This framework's design is centered around the concept of clinical modeling experience as a collaborative object, moving beyond conventional parameter-centric federated learning.
Key components of the approach include:
- Local Autonomous Clinical EHR Agents: Each hospital deploys an agent responsible for local data preprocessing and model development.
- Local Experience Refinement: These agents refine their local clinical modeling experience through three mechanisms:
- Historical memory.
- Task-specific evaluation.
- TextGrad-based prompt refinement.
- Federated Server Experience Aggregation: A central server aggregates experience across hospitals. This aggregation is guided by evidence to integrate reliable and complementary modeling experience from diverse, heterogeneous hospital environments.
- Global Meta-Prompt Distillation: The aggregated experience is distilled into global meta-prompts, which are then disseminated back to local agents for subsequent refinement.
Findings
Extensive experiments were conducted on real-world multi-hospital EHR benchmarks to evaluate FedEHR-Agents. The results demonstrated that the framework consistently outperformed both local and existing federated baselines across a variety of clinical prediction tasks. Furthermore, the framework exhibited robustness across different federation scales and when utilizing various LLM backbones.
These findings suggest that clinical modeling experience serves as a promising collaborative object within federated systems, offering an alternative or complement to conventional parameter-centric FL.
Why This Matters
The observed consistent outperformance against established baselines across diverse clinical prediction tasks, coupled with robustness across varying federation scales and LLM backbones, indicates a potential advancement in automated EHR modeling. Establishing clinical modeling experience as a collaborative object beyond traditional parameter-centric FL suggests a new direction for federated autonomous clinical intelligence.
Potential Applications
The framework points toward the development of federated autonomous clinical intelligence. This implies systems where autonomous clinical agents can collaboratively improve their EHR modeling capabilities while respecting patient data privacy constraints inherent in multi-institutional settings.