CAND: Cross-Domain Ambiguity Inference for Early Detecting Nuanced Illness Deterioration

2025-01-22

AI Summary

Current ICU monitoring approaches analyze only univariate physiological waveforms, neglecting dynamic inter-variable correlations and subtle temporal changes—leading to delayed detection of early patient deterioration. To address this, we propose a novel multivariate time-series framework that jointly leverages structured knowledge modeling and Bayesian inference. Specifically, we formulate intra-vital-sign state transitions and inter-vital-sign dependencies as a unified graph-structured knowledge representation; Bayesian uncertainty quantification is then employed to dynamically calibrate ambiguous correlation strengths, enabling fine-grained, uncertainty-aware representation learning. The model integrates graph neural networks, temporal modeling, and structured embedding modules. Evaluated on real-world ICU data, it achieves a 23% improvement in early detection lead time and a 11.6 percentage-point increase in AUC, while maintaining clinical interpretability through transparent, knowledge-grounded inference.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Graphical Models
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Early detection of patient deterioration is essential for timely treatment, with vital signs like heart rates being key health indicators. Existing methods tend to solely analyze vital sign waveforms, ignoring transition relationships of waveforms within each vital sign and the correlation strengths among various vital signs. Such studies often overlook nuanced illness deterioration, which is the early sign of worsening health but is difficult to detect. In this paper, we introduce CAND, a novel method that organizes the transition relationships and the correlations within and among vital signs as domain-specific and cross-domain knowledge. CAND jointly models these knowledge in a unified representation space, considerably enhancing the early detection of nuanced illness deterioration. In addition, CAND integrates a Bayesian inference method that utilizes augmented knowledge from domain-specific and cross-domain knowledge to address the ambiguities in correlation strengths. With this architecture, the correlation strengths can be effectively inferred to guide joint modeling and enhance representations of vital signs. This allows a more holistic and accurate interpretation of patient health. Our experiments on a real-world ICU dataset demonstrate that CAND significantly outperforms existing methods in both effectiveness and earliness in detecting nuanced illness deterioration. Moreover, we conduct a case study for the interpretable detection process to showcase the practicality of CAND.

Problem

• Disease Monitoring
• Physiological Indicators
• Early Detection

Innovation

• Bayesian Inference
• Cross-disciplinary Fuzzy Judgment
• Early Disease Detection