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

TL;DR

CAND is a novel method that improves early detection of nuanced illness deterioration by modeling transition relationships and correlations among vital signs, using a unified representation and Bayesian inference to handle ambiguities.

Contribution

The paper introduces CAND, a new approach that jointly models intra- and inter-vital sign relationships with Bayesian inference for early detection of subtle health deterioration.

Findings

CAND significantly outperforms existing methods in early detection accuracy.

CAND effectively models complex relationships among vital signs.

Case study demonstrates interpretability and practicality of CAND.

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.