A federated learning framework with knowledge graph and temporal transformer for early sepsis prediction in multi-center ICUs
arXiv:2603.15651v1 Announce Type: new Abstract: The early prediction of sepsis in intensive care unit (ICU) patients is crucial for improving survival rates. However, the development of accurate predictive models is hampered by data fragmentation across healthcare institutions and the complex, temporal nature of medical data, all under stringent privacy constraints. To address these challenges, we propose a novel framework that uniquely integrates federated learning (FL) with a medical knowledge graph and a temporal transformer model, enhanced by meta-learning capabilities. Our approach enables collaborative model training across multiple hospitals without sharing raw patient data, thereby preserving privacy. The model leverages a knowledge graph to incorporate structured medical relationships and employs a temporal transformer to capture long-range dependencies in clinical time-series data. A model-agnostic meta-learning (MAML) strategy is further incorporated to facilitate rapid ada
arXiv:2603.15651v1 Announce Type: new Abstract: The early prediction of sepsis in intensive care unit (ICU) patients is crucial for improving survival rates. However, the development of accurate predictive models is hampered by data fragmentation across healthcare institutions and the complex, temporal nature of medical data, all under stringent privacy constraints. To address these challenges, we propose a novel framework that uniquely integrates federated learning (FL) with a medical knowledge graph and a temporal transformer model, enhanced by meta-learning capabilities. Our approach enables collaborative model training across multiple hospitals without sharing raw patient data, thereby preserving privacy. The model leverages a knowledge graph to incorporate structured medical relationships and employs a temporal transformer to capture long-range dependencies in clinical time-series data. A model-agnostic meta-learning (MAML) strategy is further incorporated to facilitate rapid adaptation of the global model to local data distributions. Evaluated on the MIMIC-IV and eICU datasets, our method achieves an area under the curve (AUC) of 0.956, which represents a 22.4% improvement over conventional centralized models and a 12.7% improvement over standard federated learning, demonstrating strong predictive capability for sepsis. This work presents a reliable and privacy-preserving solution for multi-center collaborative early warning of sepsis.
Executive Summary
This article proposes a novel federated learning framework that integrates a medical knowledge graph, temporal transformer, and meta-learning capabilities for early sepsis prediction in multi-center ICUs. The framework leverages patient data distributed across hospitals without sharing raw data, preserving privacy. Evaluated on MIMIC-IV and eICU datasets, the approach achieves an AUC of 0.956, outperforming conventional centralized and federated learning models. This work presents a reliable and privacy-preserving solution for collaborative early warning of sepsis. The framework's ability to adapt to local data distributions and capture long-range dependencies in clinical time-series data demonstrates strong predictive capability for sepsis.
Key Points
- ▸ Proposes a novel federated learning framework for early sepsis prediction in ICUs
- ▸ Integrates medical knowledge graph, temporal transformer, and meta-learning capabilities
- ▸ Achieves an AUC of 0.956 on MIMIC-IV and eICU datasets, outperforming conventional models
Merits
Strength in Privacy Preservation
The framework enables collaborative model training across multiple hospitals without sharing raw patient data, preserving privacy under stringent constraints
Improved Predictive Capability
The model leverages a knowledge graph to incorporate structured medical relationships and employs a temporal transformer to capture long-range dependencies in clinical time-series data
Demerits
Complexity of Implementation
The framework's integration of multiple components, including a medical knowledge graph and temporal transformer, may require significant expertise and resources for implementation
Potential for Bias in Local Data Distributions
The model-agnostic meta-learning (MAML) strategy may not adequately address potential biases in local data distributions, which could impact the model's performance
Expert Commentary
This article presents a significant contribution to the field of artificial intelligence in healthcare, particularly in the context of data privacy and collaborative model training. The proposed framework's ability to integrate a medical knowledge graph, temporal transformer, and meta-learning capabilities demonstrates a novel approach to early sepsis prediction in multi-center ICUs. However, the complexity of implementation and potential for bias in local data distributions may limit the framework's adoption in practice. Furthermore, the article's findings may have significant policy implications for the use of artificial intelligence in healthcare.
Recommendations
- ✓ Future research should focus on addressing the complexity of implementation and potential biases in local data distributions
- ✓ Policy-makers should consider the implications of the article's findings for the use of artificial intelligence in healthcare, particularly in the context of data sharing and patient privacy