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A machine learning model for supporting symptom-based referral and diagnosis of bronchitis and pneumonia in limited resource settings

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Stokes, Katy, Castaldo, Rossana, Franzese, Monica, Salvatore, Marco, Fico, Giuseppe, Pokvic, Lejla Gurbeta, Badnjevic, Almir and Pecchia, Leandro (2021) A machine learning model for supporting symptom-based referral and diagnosis of bronchitis and pneumonia in limited resource settings. Biocybernetics and Biomedical Engineering, 41 (4). pp. 1288-1302. doi:10.1016/j.bbe.2021.09.002

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Official URL: http://dx.doi.org/10.1016/j.bbe.2021.09.002

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Abstract

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). Many scientific papers are now focusing on deep-learning methods applied to clinical images, which is unaffordable for initial patient referral in LMICs. The current study used a dataset of 4500 patients (1500 patients affected by bronchitis, 3000 by pneumonia) from a middle-income country, containing information on subject population characteristics, symptoms and laboratory test results. Manual feature selection was performed, focusing on clinical symptoms that are easily measurable in LRS and in community settings. Three common machine learning methods were tested and compared: logistic regression; decision tree and support vector machine. Models were developed through a holdout process of training-validation and testing. We focused on six clinically relevant, easily interpreted patient symptoms as best indicators for pneumonia. Our final model was a decision tree, achieving an AUC of 93%, with the advantage of being fully intelligible and easily interpreted. The performance achieved suggested that intelligible machine learning models can enhance symptom-based referral of pneumonia in LRS and in community settings.

Item Type: Journal Article
Subjects: Q Science > Q Science (General)
R Medicine > RA Public aspects of medicine
R Medicine > RC Internal medicine
Divisions: Faculty of Science > Engineering
Faculty of Medicine > Warwick Medical School
Library of Congress Subject Headings (LCSH): Pneumonia , Pneumonia -- Diagnosis -- Developing countries, Pneumonia -- Diagnosis -- Developing countries -- Simulation methods , Medical screening -- Developing countries, Bronchitis -- Diagnosis -- Developing countries -- Simulation methods , Machine learning
Journal or Publication Title: Biocybernetics and Biomedical Engineering
Publisher: Elsevier
ISSN: 0208-5216
Official Date: December 2021
Dates:
DateEvent
December 2021Published
17 September 2021Available
6 September 2021Accepted
Volume: 41
Number: 4
Page Range: pp. 1288-1302
DOI: 10.1016/j.bbe.2021.09.002
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
EP/K503848/1 [EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
EP/R511808/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
MR/N014294/1[MRC] Medical Research Councilhttp://dx.doi.org/10.13039/501100000265
Progetti di Ricerca CorrenteMinistero della Salutehttp://dx.doi.org/10.13039/501100003196

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