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Enhanced childhood diseases treatment using computational models: Systematic review of intelligent experiments
Sharareh Rostam Niakan Kalhori1, Mozhgan Tanhapour1, Marsa Gholamzadeh1
1Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
Introduction:
Precision or personalized Medicine (PM) is used for the prevention and treatment of diseases by considering a huge amount of information about individuals variables. Due to high volume of information, AI-based computational models are required. A large set of studies conducted to examine the PM approach to improve childhood clinical outcomes. Thus, the main goal of this study was to review the application of health information technology and especially artificial intelligence (AI) methods for the treatment of childhood disease using PM.
Methods:
PubMed, Scopus, Web of Science, and EMBASE databases were searched up to December 18, 2019. Articles that focused on informatics applications for childhood disease PM included in this study. Included papers were classified for qualitative analysis and interpreting results. The results were analyzed using Microsoft Excel 2019.
Results:
From 341 citations, 62 papers met our inclusion criteria. The number of published papers that used AI methods to apply for PM in childhood diseases increased from 2010 to 2019. Our results showed that most applied methods were related to machine learning discipline. In terms of clinical scope, the largest number of clinical articles are devoted to oncology. Besides, the analysis showed that genomics was the most PM approach used regarding childhood disease.
Conclusion:
This systematic review examined papers that used AI methods for applying PM approaches in childhood diseases from medical informatics perspectives. Thus, it provided new insight to researchers who are interested in knowing research needs in this field.