{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T19:50:28Z","timestamp":1774554628351,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T00:00:00Z","timestamp":1652918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Wellcome Trust","award":["217650\/Z\/19\/Z"],"award-info":[{"award-number":["217650\/Z\/19\/Z"]}]},{"name":"National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC)","award":["217650\/Z\/19\/Z"],"award-info":[{"award-number":["217650\/Z\/19\/Z"]}]},{"name":"Engineering for Development Research Fellowship provided by the Royal Academy of Engineering","award":["217650\/Z\/19\/Z"],"award-info":[{"award-number":["217650\/Z\/19\/Z"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sepsis is associated with high mortality\u2014particularly in low\u2013middle 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.<\/jats:p>","DOI":"10.3390\/s22103866","type":"journal-article","created":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T00:18:11Z","timestamp":1653005891000},"page":"3866","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Sepsis Mortality Prediction Using Wearable Monitoring in Low\u2013Middle Income Countries"],"prefix":"10.3390","volume":"22","author":[{"given":"Shadi","family":"Ghiasi","sequence":"first","affiliation":[{"name":"Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1552-5630","authenticated-orcid":false,"given":"Tingting","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0199-3783","authenticated-orcid":false,"given":"Ping","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3320-0186","authenticated-orcid":false,"given":"Jannis","family":"Hagenah","sequence":"additional","affiliation":[{"name":"Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7455-8862","authenticated-orcid":false,"given":"Phan Nguyen Quoc","family":"Khanh","sequence":"additional","affiliation":[{"name":"Oxford University Clinical Research Unit, Ho Chi Minh City 710400, Vietnam"}]},{"given":"Nguyen Van","family":"Hao","sequence":"additional","affiliation":[{"name":"Hospital of Tropical Diseases, Ho Chi Minh City 700000, Vietnam"}]},{"name":"Vital Consortium","sequence":"additional","affiliation":[]},{"given":"Louise","family":"Thwaites","sequence":"additional","affiliation":[{"name":"Oxford University Clinical Research Unit, Ho Chi Minh City 710400, Vietnam"}]},{"given":"David A.","family":"Clifton","sequence":"additional","affiliation":[{"name":"Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1001\/jama.2016.0287","article-title":"The third international consensus definitions for sepsis and septic shock (Sepsis-3)","volume":"315","author":"Singer","year":"2016","journal-title":"JAMA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/S0140-6736(19)32989-7","article-title":"Global, regional, and national sepsis incidence and mortality, 1990\u20132017: Analysis for the Global Burden of Disease Study","volume":"395","author":"Rudd","year":"2020","journal-title":"Lancet"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1164\/rccm.201504-0781OC","article-title":"Assessment of global incidence and mortality of hospital-treated sepsis. 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