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- Title
Sepsis Mortality Prediction Using Wearable Monitoring in Low–Middle Income Countries.
- Authors
Ghiasi, Shadi; Zhu, Tingting; Lu, Ping; Hagenah, Jannis; Khanh, Phan Nguyen Quoc; Hao, Nguyen Van; Thwaites, Louise; Clifton, David A.
- Abstract
Sepsis is associated with high mortality—particularly in low–middle income countries (LMICs). Critical care management of sepsis is challenging in LMICs due to the lack of care providers and the high cost of bedside monitors. Recent advances in wearable sensor technology and machine learning (ML) models in healthcare promise to deliver new ways of digital monitoring integrated with automated decision systems to reduce the mortality risk in sepsis. In this study, firstly, we aim to assess the feasibility of using wearable sensors instead of traditional bedside monitors in the sepsis care management of hospital admitted patients, and secondly, to introduce automated prediction models for the mortality prediction of sepsis patients. To this end, we continuously monitored 50 sepsis patients for nearly 24 h after their admission to the Hospital for Tropical Diseases in Vietnam. We then compared the performance and interpretability of state-of-the-art ML models for the task of mortality prediction of sepsis using the heart rate variability (HRV) signal from wearable sensors and vital signs from bedside monitors. Our results show that all ML models trained on wearable data outperformed ML models trained on data gathered from the bedside monitors for the task of mortality prediction with the highest performance (area under the precision recall curve = 0.83) achieved using time-varying features of HRV and recurrent neural networks. Our results demonstrate that the integration of automated ML prediction models with wearable technology is well suited for helping clinicians who manage sepsis patients in LMICs to reduce the mortality risk of sepsis.
- Subjects
VIETNAM; HEART beat; RECURRENT neural networks; SEPSIS; DEATH forecasting; WEARABLE technology
- Publication
Sensors (14248220), 2022, Vol 22, Issue 10, p3866
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s22103866