SepsisCalc: Integrating Clinical Calculators into Early Sepsis Prediction via Dynamic Temporal Graph Construction

2024-12-31

AI Summary

Current AI models for sepsis prediction output only scalar risk scores, lacking integration with clinical calculators such as SOFA, resulting in poor interpretability, limited robustness to sparse electronic health record (EHR) data, and low clinical trust. To address this, we propose a clinical-calculator-driven dynamic temporal graph prediction paradigm: differentiable clinical calculators (e.g., SOFA) are embedded into a graph neural network architecture, enabling missing-data-aware dynamic calculator estimation and graph-based ensemble learning. The framework jointly predicts both sepsis risk scores and organ-specific dysfunction attributions. It supports human-in-the-loop, organ-level risk explanation and intervention guidance. Evaluated on real-world EHR datasets, our method significantly outperforms state-of-the-art baselines. Deployed as an operational clinical decision-support system, it delivers real-time, interpretable organ function assessment and early warning capabilities.

Technology Category

• Reasoning under Uncertainty: Graphical Models
• Machine Learning: Calibration & Uncertainty Quantification
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Sepsis is an organ dysfunction caused by a deregulated immune response to an infection. Early sepsis prediction and identification allow for timely intervention, leading to improved clinical outcomes. Clinical calculators (e.g., the six-organ dysfunction assessment of SOFA) play a vital role in sepsis identification within clinicians' workflow, providing evidence-based risk assessments essential for sepsis diagnosis. However, artificial intelligence (AI) sepsis prediction models typically generate a single sepsis risk score without incorporating clinical calculators for assessing organ dysfunctions, making the models less convincing and transparent to clinicians. To bridge the gap, we propose to mimic clinicians' workflow with a novel framework SepsisCalc to integrate clinical calculators into the predictive model, yielding a clinically transparent and precise model for utilization in clinical settings. Practically, clinical calculators usually combine information from multiple component variables in Electronic Health Records (EHR), and might not be applicable when the variables are (partially) missing. We mitigate this issue by representing EHRs as temporal graphs and integrating a learning module to dynamically add the accurately estimated calculator to the graphs. Experimental results on real-world datasets show that the proposed model outperforms state-of-the-art methods on sepsis prediction tasks. Moreover, we developed a system to identify organ dysfunctions and potential sepsis risks, providing a human-AI interaction tool for deployment, which can help clinicians understand the prediction outputs and prepare timely interventions for the corresponding dysfunctions, paving the way for actionable clinical decision-making support for early intervention.

Problem

• Sepsis Prediction
• Clinical Calculator Integration
• Electronic Health Records

Innovation

• SepsisCalc
• AI-enhanced clinical calculators
• Dynamic time graph