Diagnosis of Community-Acquired pneumonia in children using photoplethysmography and Machine learning-based classifier

Kanwal, Kehkashan; Khalid, Syed Ghufran; Asif, Muhammad; Zafar, Farhana; Qurashi, Aisha Ghazal

doi:10.1016/j.bspc.2023.105367

Biomedical Signal Processing and Control

2024

DOI: 10.1016/j.bspc.2023.105367

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Diagnosis of Community-Acquired pneumonia in children using photoplethysmography and Machine learning-based classifier

Kehkashan Kanwal,

Syed Ghufran Khalid,

Muhammad Asif

et al.

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2024

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Cited by 2 publications

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The prediction of NICU admission and identifying influential factors in four different categories leveraging machine learning approaches

Tashakkori,

Mozdgir,

Karimi

et al. 2024

Biomedical Signal Processing and Control

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The prediction of NICU admission and identifying influential factors in four different categories leveraging machine learning approaches

Tashakkori,

Mozdgir,

Karimi

et al. 2024

Biomedical Signal Processing and Control

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Current Diagnostic Techniques for Pneumonia: A Scoping Review

Kanwal,

Asif,

Khalid

et al. 2024

Sensors

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Community-acquired pneumonia is one of the most lethal infectious diseases, especially for infants and the elderly. Given the variety of causative agents, the accurate early detection of pneumonia is an active research area. To the best of our knowledge, scoping reviews on diagnostic techniques for pneumonia are lacking. In this scoping review, three major electronic databases were searched and the resulting research was screened. We categorized these diagnostic techniques into four classes (i.e., lab-based methods, imaging-based techniques, acoustic-based techniques, and physiological-measurement-based techniques) and summarized their recent applications. Major research has been skewed towards imaging-based techniques, especially after COVID-19. Currently, chest X-rays and blood tests are the most common tools in the clinical setting to establish a diagnosis; however, there is a need to look for safe, non-invasive, and more rapid techniques for diagnosis. Recently, some non-invasive techniques based on wearable sensors achieved reasonable diagnostic accuracy that could open a new chapter for future applications. Consequently, further research and technology development are still needed for pneumonia diagnosis using non-invasive physiological parameters to attain a better point of care for pneumonia patients.

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Diagnosis of Community-Acquired pneumonia in children using photoplethysmography and Machine learning-based classifier

Cited by 2 publications

References 51 publications

The prediction of NICU admission and identifying influential factors in four different categories leveraging machine learning approaches

The prediction of NICU admission and identifying influential factors in four different categories leveraging machine learning approaches

Current Diagnostic Techniques for Pneumonia: A Scoping Review

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