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A machine learning model for supporting symptom-based referral and diagnosis of bronchitis and pneumonia in limited resource settings
Pneumonia is a leading cause of mortality in limited resource settings (LRS), which are common in low- and middle-income countries (LMICs). Accurate referrals can reduce the devastating impact of pneumonia, especially in LRS. Discriminating pneumonia from other respiratory conditions based only on symptoms is a major challenge. Machine learning has shown promise in overcoming the diagnostic difficulties of pneumonia (i.e., low specificity of symptoms, lack of accessible diagnostic tests and varied clinical presentation).
Published in the Biocybernetics and Biomedical Engineering Journal and authored by Katy Stokes, Rossana Castaldo, Monica Franzese, Marco Salvatore, Giuseppe Fico, Lejla Gurbeta Pokvic, Almir Badnjevice and Leandro Pecchia.
Read the paper at https://www.sciencedirect.com/science/article/pii/S0208521621001066?via%3Dihub