title: The power of data mining in diagnosis of childhood pneumonia.
abstract: Childhood pneumonia is the leading cause of death of children under the age of 5 years globally. Diagnostic information on the presence of infection, severity and aetiology (bacterial versus viral) is crucial for appropriate treatment. However, the derivation of such information requires advanced equipment (such as X-rays) and clinical expertise to correctly assess observational clinical signs (such as chest indrawing); both of these are often unavailable in resource-constrained settings. In this study, these challenges were addressed through the development of a suite of data mining tools, facilitating automated diagnosis through quantifiable features. Findings were validated on a large dataset comprising 780 children diagnosed with pneumonia and 801 age-matched healthy controls. Pneumonia was identified via four quantifiable vital signs (98.2% sensitivity and 97.6% specificity). Moreover, it was shown that severity can be determined through a combination of three vital signs and two lung sounds (72.4% sensitivity and 82.2% specificity); addition of a conventional biomarker (C-reactive protein) further improved severity predictions (89.1% sensitivity and 81.3% specificity). Finally, we demonstrated that aetiology can be determined using three vital signs and a newly proposed biomarker (lipocalin-2) (81.8% sensitivity and 90.6% specificity). These results suggest that a suite of carefully designed machine learning tools can be used to support multi-faceted diagnosis of childhood pneumonia in resource-constrained settings, compensating for the shortage of expensive equipment and highly trained clinicians.
authors.affiliation: Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK elina.naydenova@eng.ox.ac.uk.
authors.forename: Elina
authors.identifier: 0000-0001-7827-5803
authors.initials: E
authors.lastname: Naydenova
authors.affiliation: Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
authors.forename: Athanasios
authors.identifier: 0000-0002-0994-8100
authors.initials: A
authors.lastname: Tsanas
authors.affiliation: Child Survival Theme, Medical Research Council Unit, Serrekunda, The Gambia.
authors.forename: Stephen
authors.initials: S
authors.lastname: Howie
authors.affiliation: Nuffield Department of Medicine, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
authors.forename: Climent
authors.initials: C
authors.lastname: Casals-Pascual
authors.affiliation: Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
authors.forename: Maarten
authors.initials: M
authors.lastname: De Vos
mesh_terms: D000293:Adolescent; D015415:Biomarkers / Q000097:blood; D002097:C-Reactive Protein / Q000378:metabolism; D002648:Child; D002675:Child, Preschool; D057225:Data Mining; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D000069550:Machine Learning; D008297:Male; D011014:Pneumonia / Q000097:blood / Q000175:diagnosis* / Q000503:physiopathology; D012135:Respiratory Sounds / Q000503:physiopathology
chemical_list: D015415:Biomarkers; D002097:C-Reactive Protein
keywords: childhood pneumonia; diagnostics; machine learning