TY - GEN

T1 - Prediction of severe adverse event from vital signs for post-operative patients

AU - Gu, Ying

AU - Rasmussen, Soren M.

AU - Molgaard, Jesper

AU - Haahr-Raunkjar, Camilla

AU - Meyhoff, Christian S.

AU - Aasvang, Eske K.

AU - Sorensen, Helge B.D.

N1 - Publisher Copyright: © 2021 IEEE.

PY - 2021

Y1 - 2021

N2 - Monitoring post-operative patients is important for preventing severe adverse events (SAE), which increases morbidity and mortality. Conventional bedside monitoring system has demonstrated the difficulty in long term monitoring of those patients because majority of them are ambulatory. With development of wearable system and advanced data analytics, those patients would benefit greatly from continuous and predictive monitoring. In this study, we aim to predict SAE based on monitoring of vital signs. Heart rate, respiration rate, and blood oxygen saturation were continuously acquired by wearable devices and blood pressure was measured intermittently from 453 post-operative patients. SAEs from various complications were extracted from patients' database. The trends of vital signs were first extracted with moving average. Then four descriptive statistics were calculated from trend of each modality as features. Finally, a machine learning approach based on support vector machine was employed for prediction of SAE. It has shown the averaged accuracy of 89%, sensitivity of 80%, specificity of 93% and the area under receiver operating characteristic curve (AUROC) of 93%. These findings are promising and demonstrate the feasibility of predicting SAE from vital signs acquired with wearable devices and measured intermittently.

AB - Monitoring post-operative patients is important for preventing severe adverse events (SAE), which increases morbidity and mortality. Conventional bedside monitoring system has demonstrated the difficulty in long term monitoring of those patients because majority of them are ambulatory. With development of wearable system and advanced data analytics, those patients would benefit greatly from continuous and predictive monitoring. In this study, we aim to predict SAE based on monitoring of vital signs. Heart rate, respiration rate, and blood oxygen saturation were continuously acquired by wearable devices and blood pressure was measured intermittently from 453 post-operative patients. SAEs from various complications were extracted from patients' database. The trends of vital signs were first extracted with moving average. Then four descriptive statistics were calculated from trend of each modality as features. Finally, a machine learning approach based on support vector machine was employed for prediction of SAE. It has shown the averaged accuracy of 89%, sensitivity of 80%, specificity of 93% and the area under receiver operating characteristic curve (AUROC) of 93%. These findings are promising and demonstrate the feasibility of predicting SAE from vital signs acquired with wearable devices and measured intermittently.

U2 - 10.1109/EMBC46164.2021.9630918

DO - 10.1109/EMBC46164.2021.9630918

M3 - Article in proceedings

C2 - 34891450

AN - SCOPUS:85122531168

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

SP - 971

EP - 974

BT - 2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021

PB - IEEE

T2 - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021

Y2 - 1 November 2021 through 5 November 2021

ER -