Machine learning outperforms state-of-the-art continuous vital sign monitoring

Continuous Vital Sign Monitoring (CVSM) may allow early detection of patient deterioration with clinical impact from reduced complications. CVSM-based alert algorithms are vulnerable to missing data and artefacts, where large numbers of false positive alerts impose alert fatigue among healthcare staff, especially in low-staffed environments such as general wards. An unexplored option for overcoming missing data and improving alert precision may be using machine learning (ML) models with linear regression to summarize periods of patient data to capture critical vital sign trajectories. Four different ML-analyses of 5 unique vital signs were compared to a state-of-the-art algorithm combining vital sign duration and severity. The data consisted of continuous and semi-continuous vital signs alongside timestamps for physician-curated Serious Adverse Events (SAE) and patient metadata, from 2423 patients monitored during admission for major surgery or acute medical disease. Our results demonstrate that ML-based models improve True Positive Rates (TPR by 0.4) and False Positive Rates (FPR by 0.69). Compared to threshold-based alerts, our approach, showed superior performance in predicting SAE within 24 hours (p < 0.001) and 8 hours (p < 0.001) of an alert. The substantial improvements validate our models’ competitive edge on clinical data, when directly compared to current alerting systems, without increasing false positives.