ArticleSensors (Basel, Switzerland)2022
Sepsis Mortality Prediction Using Wearable Monitoring in Low-Middle Income Countries.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Wearable Electronic Monitoring of Vital Signs in Hospitalised Adults: A Nursing Focused Scoping Review of Clinical, Economic and Implementation Outcomes.Journal of advanced nursing · 2026Article
- From Reactive to Proactive Healthcare: Synergizing Wearable Biomarkers and Machine Learning in Digital Therapeutics.Bioengineering (Basel, Switzerland) · 2026Review
- Systems Bioengineering of Septic Shock Metabolism: Citrulline, β-Hydroxybutyrate and Plasma Biomarker-Based Phenotyping.Biomolecules · 2026Review
- Predicting Pediatric Sepsis and Mortality Using Wearable Device Data and Machine Learning in Bangladesh.The American journal of tropical medicine and hygiene · 2026Observational
- Continuous vital sign monitoring of acute Lassa fever using wearable biosensor devices in West Africa.Communications medicine · 2025Article
- Opinion on "Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare".Frontiers in immunology · 2025Article
- Improving fever management of hospitalized children with cancer: barriers, facilitators, and proposed interventions from healthcare providers in Kenya.Frontiers in oncology · 2025Article
- A novel digital health approach to improving global pediatric sepsis care in Bangladesh using wearable technology and machine learning.PLOS digital health · 2024Article
- Feasibility of wearable monitors to detect heart rate variability in children with hand, foot and mouth disease.BMC infectious diseases · 2024Observational
- Tetanus Severity Classification in Low-Middle Income Countries through ECG Wearable Sensors and a 1D-Vision Transformer.BioMedInformatics · 2024Article
- Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring.Frontiers in medicine · 2024Review
- 2D-WinSpatt-Net: A Dual Spatial Self-Attention Vision Transformer Boosts Classification of Tetanus Severity for Patients Wearing ECG Sensors in Low- and Middle-Income Countries.Sensors (Basel, Switzerland) · 2023Article
- An overview and a roadmap for artificial intelligence in hematology and oncology.Journal of cancer research and clinical oncology · 2023Review
- Exploring the Potential Use of Wearable Devices as a Prognostic Tool among Patients in Hospice Care.Medicina (Kaunas, Lithuania) · 2022Observational
- Classification of Tetanus Severity in Intensive-Care Settings for Low-Income Countries Using Wearable Sensing.Sensors (Basel, Switzerland) · 2022Article
- Review
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Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.