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

Ying Gu, Søren M. Rasmussen, Jesper Mølgaard, Camilla Haahr-Raunkjær, Christian S. Meyhoff, Eske K. Aasvang, Helge Bjarup Dissing Sørensen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society
PublisherIEEE
Publication date2021
Pages971-974
ISBN (Print)978-1-7281-1180-3
DOIs
Publication statusPublished - 2021
Event43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Virtual event
Duration: 1 Nov 20215 Nov 2021

Conference

Conference43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
LocationVirtual event
Period01/11/202105/11/2021
SeriesAnnual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual International Conference
ISSN2694-0604

Keywords

  • Humans
  • Monitoring, Physiologic
  • Oxygen Saturation
  • Respiratory Rate
  • Vital Signs
  • Wearable Electronic Devices

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