Enhanced childhood diseases treatment using computational models: Systematic review of intelligent experiments heading to precision medicine

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 341citations, 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.

[1]  D. Amaral,et al.  Metabolomics as a Tool for Discovery of Biomarkers of Autism Spectrum Disorder in the Blood Plasma of Children , 2014, PloS one.

[2]  N. Mishra,et al.  Deep Learning/Artificial Intelligence and Blood-Based DNA Epigenomic Prediction of Cerebral Palsy , 2019, International journal of molecular sciences.

[3]  P. Rinaldo,et al.  Precision newborn screening for lysosomal disorders , 2018, Genetics in Medicine.

[4]  Max L. Balter,et al.  The growing role of precision and personalized medicine for cancer treatment , 2018, Technology.

[5]  Simon M. Lin,et al.  Comorbidities in Childhood Celiac Disease: A Phenome Wide Association Study Using the Electronic Health Record , 2018, Journal of pediatric gastroenterology and nutrition.

[6]  Maria A. Zuluaga,et al.  Detecting Clinically Meaningful Shape Clusters in Medical Image Data: Metrics Analysis for Hierarchical Clustering Applied to Healthy and Pathological Aortic Arches , 2017, IEEE Transactions on Biomedical Engineering.

[7]  J. Krischer,et al.  Eosinophilic oesophagitis endotype classification by molecular, clinical, and histopathological analyses: a cross-sectional study. , 2018, The lancet. Gastroenterology & hepatology.

[8]  I. Buchan,et al.  Challenges in interpreting allergen microarrays in relation to clinical symptoms: A machine learning approach , 2013, Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology.

[9]  A. Raj,et al.  Countdown to 2030: tracking progress towards universal coverage for reproductive, maternal, newborn, and child health , 2018, The Lancet.

[10]  Sandeep Kaushik,et al.  Big data in healthcare: management, analysis and future prospects , 2019, Journal of Big Data.

[11]  H. Wong.  Intensive care medicine in 2050: precision medicine , 2017, Intensive Care Medicine.

[12]  S. Snapper,et al.  An algorithm for the classification of mRNA patterns in eosinophilic esophagitis: Integration of machine learning , 2017, The Journal of allergy and clinical immunology.

[13]  A. Chinnaiyan,et al.  Precision medicine in pediatric oncology: Lessons learned and next steps , 2017, Pediatric blood & cancer.

[14]  A. Pulvirenti,et al.  A Subset of Patients With Autism Spectrum Disorders Show a Distinctive Metabolic Profile by Dried Blood Spot Analyses , 2018, Front. Psychiatry.

[15]  George Hripcsak,et al.  Deep Phenotyping on Electronic Health Records Facilitates Genetic Diagnosis by Clinical Exomes. , 2018, American journal of human genetics.

[16]  P. Techakesari,et al.  Healthcare professionals' attitudes toward cancer precision medicine: A systematic review. , 2019, Seminars in oncology.

[17]  Jipan Xie,et al.  Developing a Risk Score to Guide Individualized Treatment Selection in Attention Deficit/Hyperactivity Disorder. , 2015, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[18]  J. Fernández-Recio,et al.  Structural and Computational Characterization of Disease-Related Mutations Involved in Protein-Protein Interfaces , 2019, International journal of molecular sciences.

[19]  Weishi Zheng,et al.  Discrimination of the behavioural dynamics of visually impaired infants via deep learning , 2019, Nature Biomedical Engineering.

[20]  Lian-pan Wu,et al.  Treatment response prediction of rehabilitation program in children with cerebral palsy using radiomics strategy: protocol for a multicenter prospective cohort study in west China. , 2019, Quantitative imaging in medicine and surgery.

[21]  R. Nath,et al.  Personalized estimation of dose to red bone marrow and the associated leukaemia risk attributable to pelvic kilo-voltage cone beam computed tomography scans in image-guided radiotherapy , 2012, Physics in medicine and biology.

[22]  Kenneth D. Mandl,et al.  Creating a scalable clinical pharmacogenomics service with automated interpretation and medical record result integration – experience from a pediatric tertiary care facility , 2017, J. Am. Medical Informatics Assoc..

[23]  Chanhyun Park,et al.  Examining the heterogeneity of treatment patterns in attention deficit hyperactivity disorder among children and adolescents in the Texas Medicaid population: modeling suboptimal treatment response , 2019, Journal of medical economics.

[24]  Eva Petkova,et al.  Optimising treatment decision rules through generated effect modifiers: a precision medicine tutorial , 2019, BJPsych open.

[25]  P. Shekelle,et al.  Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement , 2015, Systematic Reviews.

[26]  J. Hirsch,et al.  Can Depression be Diagnosed by Response to Mother's Face? A Personalized Attachment-Based Paradigm for Diagnostic fMRI , 2011, PloS one.

[27]  I. König,et al.  What is precision medicine? , 2017, European Respiratory Journal.

[28]  Tugba Taskaya-Temizel,et al.  A Bayesian Estimation Framework for Pharmacogenomics Driven Warfarin Dosing: A Comparative Study , 2015, IEEE Journal of Biomedical and Health Informatics.

[29]  W. Foulkes,et al.  Retrospective evaluation of a decision‐support algorithm (MIPOGG) for genetic referrals for children with neuroblastic tumors , 2018, Pediatric blood & cancer.

[30]  Yujiang Wang,et al.  Artificial intelligence for precision medicine in neurodevelopmental disorders , 2019, npj Digital Medicine.

[31]  Mindy K. Ross,et al.  Discovering Pediatric Asthma Phenotypes on the Basis of Response to Controller Medication Using Machine Learning , 2018, Annals of the American Thoracic Society.

[32]  Rui Chen,et al.  Systems biology: personalized medicine for the future? , 2012, Current opinion in pharmacology.

[33]  Bertalan Meskó,et al.  The role of artificial intelligence in precision medicine , 2017 .

[34]  R. Black,et al.  Current and Future Challenges for Children Across the World. , 2019, JAMA.

[35]  Teng Jiang,et al.  Toward precision medicine in neurological diseases. , 2016, Annals of translational medicine.

[36]  D. Montaner,et al.  Quantitative modeling of clinical, cellular, and extracellular matrix variables suggest prognostic indicators in cancer: a model in neuroblastoma , 2014, Pediatric Research.

[37]  V. Mazzaferro,et al.  Radioembolization of hepatocarcinoma with 90Y glass microspheres: development of an individualized treatment planning strategy based on dosimetry and radiobiology , 2015, European Journal of Nuclear Medicine and Molecular Imaging.

[38]  Eyal Dassau,et al.  Proposed Clinical Application for Tuning Fuzzy Logic Controller of artificial Pancreas Utilizing a Personalization Factor , 2010, Journal of diabetes science and technology.

[39]  Shahin Akhondzadeh.  Personalized Medicine: A Tailor Made Medicine , 2014, Avicenna journal of medical biotechnology.

[40]  Simeon M. Wong,et al.  Presurgical thalamocortical connectivity is associated with response to vagus nerve stimulation in children with intractable epilepsy , 2017, NeuroImage: Clinical.

[41]  M. Relling,et al.  Pharmacogenetics for Safe Codeine Use in Sickle Cell Disease , 2016, Pediatrics.

[42]  Xiaohang Wu,et al.  Diagnostic Efficacy and Therapeutic Decision-making Capacity of an Artificial Intelligence Platform for Childhood Cataracts in Eye Clinics: A Multicentre Randomized Controlled Trial , 2019, EClinicalMedicine.

[43]  A. Chinnaiyan,et al.  Precision oncology in the age of integrative genomics , 2018, Nature Biotechnology.

[44]  Benjamin S. Glicksberg,et al.  Development and clinical application of an integrative genomic approach to personalized cancer therapy , 2016, Genome Medicine.

[45]  Daniel Cremers,et al.  Radiomics in radiooncology - Challenging the medical physicist. , 2018, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[46]  Wei Chen,et al.  Mimo pillow : an intelligent cushion designed with maternal heart beat vibrations for comforting newborn infants , 2014 .

[47]  T. McCoy,et al.  Association between child psychiatric emergency room outcomes and dimensions of psychopathology. , 2019, General hospital psychiatry.

[48]  Samina Raza Abidi,et al.  Clinical Guideline-Driven Personalized Self-Management Diary for Paediatric Cancer Survivors , 2014, MIE.

[49]  Nicholas J Schork,et al.  Artificial Intelligence and Personalized Medicine. , 2019, Cancer treatment and research.

[50]  John Reynders,et al.  Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation , 2019, Science Translational Medicine.

[51]  H. Lehrach,et al.  Cancer Precision Medicine: Why More Is More and DNA Is Not Enough , 2017, Public Health Genomics.

[52]  M. Verma.  Personalized Medicine and Cancer , 2012, Journal of personalized medicine.

[53]  R. Chadwick,et al.  Epigenetics and personalized medicine: prospects and ethical issues. , 2013, Personalized medicine.

[54]  M. Rubinstein,et al.  In vivo, in vitro and in silico correlations of four de novo SCN1A missense mutations , 2019, PloS one.

[55]  John Hughes,et al.  Disease severity classification using quantitative magnetic resonance imaging data of cartilage in femoroacetabular impingement , 2017, Statistics in medicine.

[56]  Z. Bhutta,et al.  Setting an implementation research agenda for Canadian investments in global maternal, newborn, child and adolescent health: a research prioritization exercise. , 2017, CMAJ open.

[57]  S. Nelson,et al.  A Path to Implement Precision Child Health Cardiovascular Medicine , 2017, Front. Cardiovasc. Med..

[58]  H. Blasco-Fontecilla.  Clinical utility of pharmacogenetic testing in children and adolescents with severe mental disorders , 2018, Journal of Neural Transmission.

[59]  J Duncan,et al.  Brain responses to biological motion predict treatment outcome in young children with autism , 2016, Translational psychiatry.

[60]  Jia Xu,et al.  Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives , 2019, Human Genetics.

[61]  P. Obermeier,et al.  Enabling Precision Medicine With Digital Case Classification at the Point-of-Care☆ , 2016, EBioMedicine.

[62]  Gavin R. Oliver,et al.  Experience with precision genomics and tumor board, indicates frequent target identification, but barriers to delivery , 2017, Oncotarget.

[63]  S. Lui,et al.  Psychoradiologic Utility of MR Imaging for Diagnosis of Attention Deficit Hyperactivity Disorder: A Radiomics Analysis. , 2017, Radiology.

[64]  Yves A. Lussier,et al.  A genome-by-environment interaction classifier for precision medicine: personal transcriptome response to rhinovirus identifies children prone to asthma exacerbations , 2017, J. Am. Medical Informatics Assoc..

[65]  Juha Pärkkä,et al.  Personalization Algorithm for Real-Time Activity Recognition Using PDA, Wireless Motion Bands, and Binary Decision Tree , 2010, IEEE Transactions on Information Technology in Biomedicine.

[66]  D. French,et al.  Population Pharmacokinetics of Busulfan in Pediatric and Young Adult Patients Undergoing Hematopoietic Cell Transplant: A Model-Based Dosing Algorithm for Personalized Therapy and Implementation Into Routine Clinical Use , 2015, Therapeutic drug monitoring.

[67]  A. Chinnaiyan,et al.  Development of the CNS TAP tool for the selection of precision medicine therapies in neuro-oncology , 2018, Journal of Neuro-Oncology.

[68]  Q. Morris,et al.  The Biologic Basis of Clinical Heterogeneity in Juvenile Idiopathic Arthritis , 2014, Arthritis & rheumatology.

[69]  Jeremy J. W. Chen,et al.  R331W Missense Mutation of Oncogene YAP1 Is a Germline Risk Allele for Lung Adenocarcinoma With Medical Actionability. , 2015, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[70]  G. Thompson,et al.  Cytokines and Chemokines in Pediatric Appendicitis: A Multiplex Analysis of Inflammatory Protein Mediators , 2019, Mediators of inflammation.

[71]  Ayman El-Baz,et al.  Using resting state functional MRI to build a personalized autism diagnosis system , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[72]  Jiajia Chen,et al.  Informatics for Precision Medicine and Healthcare. , 2017, Advances in experimental medicine and biology.

[73]  R. Neapolitan,et al.  A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis , 2019, PloS one.

[74]  Mario Castro,et al.  Predicting Intermediate Phenotypes in Asthma Using Bronchoalveolar Lavage‐Derived Cytokines , 2010, Clinical and translational science.

[75]  Feng Chen,et al.  Machine Learning Models for Genetic Risk Assessment of Infants with Non-syndromic Orofacial Cleft , 2018, Genom. Proteom. Bioinform..

[76]  B. Frey,et al.  The human splicing code reveals new insights into the genetic determinants of disease , 2015, Science.

[77]  S. Bremberg.  Health promotion at Swedish Child Health Centres , 2000, Acta paediatrica (Oslo, Norway : 1992). Supplement.

[78]  V. Ng,et al.  Predicting ideal outcome after pediatric liver transplantation: An exploratory study using machine learning analyses to leverage Studies of Pediatric Liver Transplantation Data , 2019, Pediatric transplantation.

[79]  Sue C. Bratton,et al.  A Meta‐Analytic Review of Child‐Centered Play Therapy Approaches , 2015 .

[80]  Julie A. Johnson,et al.  Implementation of Standardized Clinical Processes for TPMT Testing in a Diverse Multidisciplinary Population: Challenges and Lessons Learned , 2018, Clinical and translational science.

[81]  Ying Zhang,et al.  Personalized prediction model for seizure‐free epilepsy with levetiracetam therapy: a retrospective data analysis using support vector machine , 2018, British journal of clinical pharmacology.

[82]  Mariella G. Filbin,et al.  Clinical targeted exome-based sequencing in combination with genome-wide copy number profiling: precision medicine analysis of 203 pediatric brain tumors , 2017, Neuro-oncology.

[83]  Nigam H. Shah,et al.  Addressing vital sign alarm fatigue using personalized alarm thresholds , 2019, PSB.

[84]  P. Northcott,et al.  Genomic Analysis of Childhood Brain Tumors: Methods for Genome-Wide Discovery and Precision Medicine Become Mainstream. , 2017, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.