ArticleHealthcare technology letters2020
Severity detection tool for patients with infectious disease.
Article in Healthcare technology letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
8 citing papers in PubMed, 1 citations in OpenAlex.
- Tetanus Severity Classification in Low-Middle Income Countries through ECG Wearable Sensors and a 1D-Vision Transformer.BioMedInformatics · 2024Article
- Heart Rate Variability Measured from Wearable Devices as a Marker of Disease Severity in Tetanus.The American journal of tropical medicine and hygiene · 2024Article
- 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
- Permutation Entropy Analysis to Intracranial Hypertension from a Porcine Model.Entropy (Basel, Switzerland) · 2023Article
- Classification of Tetanus Severity in Intensive-Care Settings for Low-Income Countries Using Wearable Sensing.Sensors (Basel, Switzerland) · 2022Article
- Novel Clinical Monitoring Approaches for Reemergence of Diphtheria Myocarditis, Vietnam.Emerging infectious diseases · 2022Review
- vital_sqi: A Python package for physiological signal quality control.Frontiers in physiology · 2022Article
- Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases.Computational and mathematical methods in medicine · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 5 institutions in 4 countries.
Funding
Abstract
Hand foot and mouth disease (HFMD) and tetanus are serious infectious diseases in low- and middle-income countries. Tetanus, in particular, has a high mortality rate and its treatment is resource-demanding. Furthermore, HFMD often affects a large number of infants and young children. As a result, its treatment consumes enormous healthcare resources, especially when outbreaks occur. Autonomic nervous system dysfunction (ANSD) is the main cause of death for both HFMD and tetanus patients. However, early detection of ANSD is a difficult and challenging problem. The authors aim to provide a proof-of-principle to detect the ANSD level automatically by applying machine learning techniques to physiological patient data, such as electrocardiogram waveforms, which can be collected using low-cost wearable sensors. Efficient features are extracted that encode variations in the waveforms in the time and frequency domains. The proposed approach is validated on multiple datasets of HFMD and tetanus patients in Vietnam. Results show that encouraging performance is achieved. Moreover, the proposed features are simple, more generalisable and outperformed the standard heart rate variability analysis. The proposed approach would facilitate both the diagnosis and treatment of infectious diseases in low- and middle-income countries, and thereby improve patient care.
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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.