Severity detection tool for patients with infectious disease

Healthcare Technology Letters

article

Girmaw Abebe Tadesse¹  Tingting Zhu¹  Nhan Le Nguyen Thanh³  Nguyen Thanh Hung³  Ha Thi Hai Duong⁴  Truong Huu Khanh³  Pham Van Quang³  Duc Duong Tran⁴  Lam Minh Yen⁵  Rogier Van Doorn⁶  Nguyen Van Hao⁴  John Prince¹  Hamza Javed¹  Dani Kiyasseh¹  Le Van Tan⁵  Louise Thwaites⁵  David A. Clifton¹ 

[1] Institute of Biomedical Engineering, University of Oxford;IBM Research Africa;Children's Hospital Number 1;Hospital for Tropical Diseases;Oxford Clinical Research Unit;Oxford University Clinical Research Unit;Centre for Tropical Medicine and Global Health, Oxford University

关键词: support vector machines;    cardiology;    electrocardiography;    patient care;    neurophysiology;    patient diagnosis;    diseases;    learning (artificial intelligence);    patient treatment;    medical signal processing;    medical computing;    health care;    feature extraction;    severity detection tool;    infectious disease;    HFMD;    serious infectious diseases;    middle-income countries;    high mortality rate;    resource-demanding;    young children;    enormous healthcare resources;    autonomic nervous system dysfunction;    tetanus patients;    difficult problem;    proof-of-principle;    ANSD level;    physiological patient data;    electrocardiogram;    photoplethysmogram waveforms;    low-cost wearable sensors;    frequency domains;    support vector machine;    classifying ANSD levels;    standard heart rate variability analysis;    patient care;   

DOI  :  10.1049/htl.2019.0030

学科分类：肠胃与肝脏病学

来源: Wiley

【 摘 要 】

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.

【 授权许可】

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