Expert Augmented Prediction of Circulatory and Respiratory Instability from High Resolution Vital Signs.

Cohort studyNPJ digital medicine· Jul 2026

Expert Augmented Prediction of Circulatory and Respiratory Instability from High Resolution Vital Signs.

Luhao Wang, Bin Gu, Yao Nie … Xiangdong Guan

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Pulmonology & Critical CareCohort studyQ1IF 18.0

Machine learning models accurately predict circulatory and respiratory instability in intensive care units.

Expert Augmented Prediction of Circulatory and Respiratory Instability from High Resolution Vital Signs.

Luhao Wang … Xiangdong Guan

NPJ digital medicine · Jul 2026 · Q1 in General Medicine

Methods

Machine‑learning models were trained on 627,958 h of continuous vital‑sign data from 1702 intensive care units patients at the First Affiliated Hospital of Sun Yat‑sen University and externally validated in the MIMIC‑III cohort.

n = 1702 ICU patients

Results

AUROC > 0.8

The models were highly accurate at predicting circulatory and respiratory instability

n = 1702 ICU patients

More results

Leveraging routinely collected high-frequency vital-sign monitoring, we developed an interpretable, expert-augmented early warning system based on 1-second-resolution heart rate, blood pressure, respiratory rate, and oxygen saturation data.

More results

Increasing temporal resolution improved predictive accuracy, with trend-based features contributing most strongly to model predictions.

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Conclusion

Expert-Augmented Early Warning System generated accurate, low-frequency alerts with transparent explanations aligned with bedside monitoring, and may provide a scalable framework for real-time circulatory and respiratory instability detection in intensive care units.

Expert Augmented Prediction of Circulatory and Respiratory Instability from High Resolution Vital Signs.

Expert Augmented Prediction of Circulatory and Respiratory Instability from High Resolution Vital Signs.

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