Machine learning for child and adolescent health: A systematic review

CONTEXT: In the last few decades, data acquisition and processing has seen tremendous amount of growth, thus sparking interest in machine learning (ML) within the health care system. OBJECTIVE: Our aim for this review is to provide an evidence map of the current available evidence on ML in pediatrics and adolescent medicine and provide insight for future research. DATA SOURCES: A literature search was conducted by using Medline, the Cochrane Library, the Cumulative Index to Nursing and Allied Health Literature Plus, Web of Science Library, and EBSCO Dentistry & Oral Science Source. STUDY SELECTION: Articles in which an ML model was assessed for the diagnosis, prediction, or management of any condition in children and adolescents (0-18 years) were included. DATA EXTRACTION: Data were extracted for year of publication, geographical location, age range, number of participants, disease or condition under investigation, study methodology, reference standard, type, category, and performance of ML algorithms. RESULTS: The review included 363 studies, with subspecialties such as psychiatry, neonatology, and neurology having the most literature. A majority of the studies were from high-income (82%; n = 296) and upper middle-income countries (15%; n = 56), whereas only 3% (n = 11) were from low middle-income countries. Neural networks and ensemble methods were most commonly tested in the 1990s, whereas deep learning and clustering emerged rapidly in the current decade. LIMITATIONS: Only studies conducted in the English language could be used in this review. CONCLUSIONS: The interest in ML has been growing across various subspecialties and countries, suggesting a potential role in health service delivery for children and adolescents in the years to come.