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Data harnessing to nurture the human mind for a tailored approach to the child

Abstract

Big data in pediatrics is an ocean of structured and unstructured data. Big data analysis helps to dive into the ocean of data to filter out information that can guide pediatricians in their decision making, precision diagnosis, and targeted therapy. In addition, big data and its analysis have helped in the surveillance, prevention, and performance of the health system. There has been a considerable amount of work in pediatrics that we have tried to highlight in this review and some of it has been already incorporated into the health system. Work in specialties of pediatrics is still forthcoming with the creation of a common data model and amalgamation of the huge “omics” database. The physicians entrusted with the care of children must be aware of the outcome so that they can play a role to ensure that big data algorithms have a clinically relevant effect in improving the health of their patients. They will apply the outcome of big data and its analysis in patient care through clinical algorithms or with the help of embedded clinical support alerts from the electronic medical records.

Impact

  • Big data in pediatrics include structured, unstructured data, waveform data, biological, and social data.

  • Big data analytics has unraveled significant information from these databases.

  • This is changing how pediatricians will look at the body of available evidence and translate it into their clinical practice.

  • Data harnessed so far is implemented in certain fields while in others it is in the process of development to become a clinical adjunct to the physician.

  • Common databases are being prepared for future work.

  • Diagnostic and prediction models when incorporated into the health system will guide the pediatrician to a targeted approach to diagnosis and therapy.

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Fig. 1: Schematic representation of Big data.
Fig. 2: From Medical informatics to Precision Medicine.

Data availability

The datasets generated during the current study are included in this published article and available from the corresponding author upon reasonable request. In certain cases, hyperlinks to publicly archived datasets generated during the study are available.

References

  1. Smallwood, C. D. Monitoring big data during mechanical ventilation in the ICU. Respir. Care 65, 894–910 (2020).

    Article  PubMed  Google Scholar 

  2. Mashingaidze, K. & Backhouse, J. The relationships between definitions of big data, business intelligence and business analytics. Int. J. Bus. Inf. Syst. 26, 488–505 (2017).

    Google Scholar 

  3. Belle, A. et al. Big data analytics in healthcare. Biomed. Res. Int. 2015, 370194, https://doi.org/10.1155/2015/370194 (2015).

  4. Luo, J., Wu, M., Gopukumar, D., & Zhao, Y. Big data application in biomedical research and health care: a literature review. Biomed. Inform. Insights 8, 1–10. https://doi.org/10.4137/BII.S31559 (2016).

  5. Harb, H., Mroue, H., Mansour, A., Nasser, A., & Cruz, E. M. A Hadoop-based platform for patient classification and disease diagnosis in healthcare applications. Sensors (Basel, Switzerland) 20, 1931, https://doi.org/10.3390/S20071931 (2020).

  6. Batarseh, F. A. & Latif, E. A. Assessing the quality of service using big data analytics: with application to healthcare. Big Data Res. 4, 13–24 (2016).

    Article  Google Scholar 

  7. Chollet, F. Deep Learning with Python 2nd edn 7–8 (Manning Publications Co., 2017).

  8. Francois-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G. & Pineau, J. An introduction to deep reinforcement learning. Found. Trends Mach. Learn 11, 219–354 (2018).

    Article  Google Scholar 

  9. Anderson, T. M. et al. Maternal smoking before and during pregnancy and the risk of sudden unexpected infant death. Pediatrics 143, e20183325, https://doi.org/10.1542/PEDS.2018-3325 (2019).

  10. The Lancet Diabetes Endocrinology Childhood obesity: a growing pandemic. Lancet Diabetes Endocrinol. 10, 1 (2022).

    Article  CAS  PubMed  Google Scholar 

  11. Pang, X., Forrest, C. B., Masino, A. J. & Le-Scherban, F. Prediction of early childhood obesity with machine learning and electronic health record data. Int. J. Med. Inform. 150, 104454, http://www.elsevier.com/inca/publications/store/5/0/6/0/4/0/ (2021).

    Article  PubMed  Google Scholar 

  12. Hammond, R. et al. Predicting childhood obesity using electronic health records and publicly available data. PLoS One 14, e0215571 (2019).

  13. Fan, H., Li, L., Gilbert, R., O’Callaghan, F. & Wijlaars, L. A machine learning approach to identify cases of cerebral palsy using the UK primary care database. Lancet 392, S33 (2018).

    Article  Google Scholar 

  14. Bone, D. et al. Applying machine learning to facilitate autism diagnostics: pitfalls and promises. J. Autism Dev. Disord. 45, 1121–1136 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Bledsoe, J. C. et al. Diagnostic classification of ADHD versus control: support vector machine classification using brief neuropsychological assessment. J. Atten. Disord. 24, 1547–1556 (2020).

    Article  PubMed  Google Scholar 

  16. Zacharek, S. J., Kribakaran, S., Kitt, E. R. & Gee, D. G. Leveraging big data to map neurodevelopmental trajectories in pediatric anxiety. Dev. Cogn. Neurosci. 50, 100974 (2021).

  17. Pruett, D. G. et al. Identifying developmental stuttering and associated comorbidities in electronic health records and creating a phenome risk classifier. J. Fluen. Disord. 68, 105847 (2021).

    Article  Google Scholar 

  18. Feng, J., Lee, J., Vesoulis, Z. A. & Li Fuhai, A. O. Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data. npj Digit. Med. 4, 108, https://www.nature.com/npjdigitalmed/ (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Lei, J. et al. Risk identification of bronchopulmonary dysplasia in premature infants based on machine learning. Front. Pediatr. 9, 719352, https://www.frontiersin.org/journals/pediatrics (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Coyner, A. S. et al. A risk model for early detection of treatment requiring retinopathy of prematurity using a deep learning-derived vascular severity score. Investig. Ophthalmol. Vis. Sci. 62, https://iovs.arvojournals.org/article.aspx?articleid=2773154 (2021).

  21. Masino, A. J. et al. Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data. PLoS One 14, e0212665 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Suresh, S. Big data and predictive analytics. Applications in the care of children. Pediatr. Clin. North Am. 63, 357–366 (2016).

    Article  PubMed  Google Scholar 

  23. Aczon, M. D., Ledbetter, D. R., Laksana, E., Ho, L. V & Wetzel, R. C. Continuous prediction of mortality in the PICU: a recurrent neural network model in a single-center dataset. Pediatr. Crit. Care Med. 22, 519–529, http://journals.lww.com/pccmjournal (2021).

  24. Ehrlich, L., Ledbetter, D., Aczon, M., Laksana, E. & Wetzel, R. Continuous risk of desaturation within the next hour prediction using a recurrent neural network. Crit. Care Med. 49, 480 (2021).

    Article  Google Scholar 

  25. Pappy, G., Ledbetter, D., Aczon, M. & Wetzel, R. Early prediction of HFNC failure in the pediatric ICU using a recurrent neural network. Crit. Care Med. 49, 501 (2021).

    Article  Google Scholar 

  26. Comoretto, R. I. et al. Predicting hemodynamic failure development in PICU using machine learning techniques. Diagnostics 11, 1299 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Le, S. et al. Pediatric severe sepsis prediction using machine learning. Front. Pediatr. 7, 413 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Sandokji, I. et al. A time-updated, parsimonious model to predict AKI in hospitalized children. J. Am. Soc. Nephrol. 31, 1348–1357 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Dong, J. et al. Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care. Crit. Care 25, 288 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Goto, T., Camargo, C. A., Faridi, M. K., Hasegawa, K. & Freishtat, R. J. Machine learning-based prediction of clinical outcomes for children during emergency department triage. JAMA Netw. Open 2, e186937 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Sills, M. R., Ozkaynak, M. & Jang, H. Predicting hospitalization of pediatric asthma patients in emergency departments using machine learning. Int. J. Med. Inform. 151, 104468 (2021).

    Article  PubMed  Google Scholar 

  32. Mayampurath, A. et al. Predicting deterioration in hospitalized children using machine learning. Crit. Care Med. 49, 523 (2021).

    Article  Google Scholar 

  33. Major, A., Cox, S. M. & Volchenboum, S. L. Using big data in pediatric oncology: current applications and future directions. Semin. Oncol. 47, 56–64 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  34. NCI Cancer Research Data Commons | CBIIT (accessed 25 February 2022); https://datascience.cancer.gov/data-commons.

  35. Feng, C. et al. A deep-learning model with the attention mechanism could rigorously predict survivals in neuroblastoma. Front. Oncol. 11, 653863 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Kashef, A., Khatibi, T. & Mehrvar, A. Treatment outcome classification of pediatric acute lymphoblastic leukemia patients with clinical and medical data using machine learning: a case study at MAHAK hospital. Inform. Med. Unlocked 20, 100399 (2020).

    Article  Google Scholar 

  37. Alloy, A. P. et al. Novel pediatric AML patient risk stratification by inferred protein activity through integrative network analysis and machine learning. Cancer Res. 81, 1 (2021).

  38. Naydenova, E., Tsanas, A., Howie, S., Casals-Pascual, C. & De Vos, M. The power of data mining in diagnosis of childhood pneumonia. J. R. Soc. Interface 13, 20160266 (2016).

  39. Morang’a, C. M. et al. Machine learning approaches classify clinical malaria outcomes based on haematological parameters. BMC Med. 18, 375 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Porras, A. R., Rosenbaum, K., Summar, M., Tor-Diez, C. & Linguraru, M. G. Development and evaluation of a machine learning-based point-of-care screening tool for genetic syndromes in children: a multinational retrospective study. Lancet Digit. Heal. 3, e635–e643 (2021).

    Article  Google Scholar 

  41. Gaffar, S., Gearhart, A. S. & Chang, A. C. The next frontier in pediatric cardiology: artificial intelligence. Pediatr. Clin. North Am. 67, 995–1009 (2020).

    Article  PubMed  Google Scholar 

  42. Tandon, A. & De Ferranti, S. D. Wearable biosensors in pediatric cardiovascular disease: promises and pitfalls toward generating actionable insights. Circulation 140, 350–352 (2019).

    Article  CAS  PubMed  Google Scholar 

  43. Bos, J. M. et al. Artificial intelligence-enabled assessment of the heart rate corrected qt interval using a mobile electrocardiogram device in children and adolescents. Hear. Rhythm 18, S82–S83 (2021).

    Article  Google Scholar 

  44. Siontis, K. et al. Detection of hypertrophic cardiomyopathy by artificial intelligence-enabled electrocardiography in children and adolescents. J. Am. Coll. Cardiol. 77, 3247 (2021).

    Article  Google Scholar 

  45. Mori, H. et al. Diagnosing atrial septal defect from electrocardiogram with deep learning. Pediatr. Cardiol. 42, 1379–1387 (2021).

    Article  PubMed  Google Scholar 

  46. Martins, J. F. B. S. et al. Towards automatic diagnosis of rheumatic heart disease on echocardiographic exams through video-based deep learning. J. Am. Med. Inform. Assoc. 28, 1834–1842, http://jamia.oxfordjournals.org/content/22/e1 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Hwang, S. T., Kang, C. H. & Ahn, K.-S. External validation of X-ray image-based artificial intelligence bone age automatic measurement software for growing children. Skelet. Radio. 47, 1320 (2018).

    Google Scholar 

  48. Chen, K. C. et al. Diagnosis of common pulmonary diseases in children by X-ray images and deep learning. Sci. Rep. 10, 17374 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Meda, K. C., Milla, S. S. & Rostad, B. S. Artificial intelligence research within reach: an object detection model to identify rickets on pediatric wrist radiographs. Pediatr. Radiol. 51, 782–791 (2021).

    Article  PubMed  Google Scholar 

  50. Summers, R. M. Deep learning lends a hand to pediatric radiology. Radiology 287, 323 (2018).

    Article  PubMed  Google Scholar 

  51. Smail, L. C., Dhindsa, K., Braga, L. H., Becker, S. & Sonnadara, R. R. Using deep learning algorithms to grade hydronephrosis severity: toward a clinical adjunct. Front. Pediatr. 8, 1, https://www.frontiersin.org/journals/pediatrics (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Khammad, V. et al. Application of machine learning algorithms for the diagnosis of primary brain tumors. J. Clin. Oncol. 38, 2533–2533 (2020).

  53. Dong, B. et al. Development and evaluation of a leukemia diagnosis system using deep learning in real clinical scenarios. Front. Pediatr. 9, 693676 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Yih, W. K. et al. Intussusception risk after rotavirus vaccination in U.S. infants. N. Engl. J. Med. 370, 503–512 (2014).

    Article  CAS  PubMed  Google Scholar 

  55. Prasad, V., Kendrick, D., Sayal, K., Thomas, S. L. & West, J. Injury among children and young adults with epilepsy. Pediatrics 133, 827–835 (2014).

    Article  PubMed  Google Scholar 

  56. Yu, Z. et al. Predicting adverse drug events in chinese pediatric inpatients with the associated risk factors: a machine learning study. Front. Pharmacol. 12, 659099 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Gregornik, D., Salyakina, D., Brown, M., Roiko, S. & Ramos, K. Pediatric pharmacogenomics: challenges and opportunities: on behalf of the Sanford Children’s Genomic Medicine Consortium. Pharmacogenomics J. 211, 8–19 (2020).

    Google Scholar 

  58. Moehring, R. W. et al. Development of a machine learning model using electronic health record data to identify antibiotic use among hospitalized patients. JAMA Netw. Open 4, e213460 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Talukder, A. & Ahammed, B. Machine learning algorithms for predicting malnutrition among under-five children in Bangladesh. Nutrition 78, 110861 (2020).

    Article  PubMed  Google Scholar 

  60. Adegbosin, A. E., Stantic, B. & Sun, J. Efficacy of deep learning methods for predicting under-five mortality in 34 low-income and middle-income countries. BMJ Open 10, e034524 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Fenta, H. M., Zewotir, T. & Muluneh, E. K. A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones. BMC Med. Inform. Decis. Mak. 21, 291 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Methun, M. I. H., Kabir, A., Islam, S., Hossain, M. I. & Darda, M. A. A machine learning logistic classifier approach for identifying the determinants of under-5 child morbidity in Bangladesh. Clin. Epidemiol. Glob. Heal. 12, 100812 (2021).

    Article  CAS  Google Scholar 

  63. Kurowski, B. G. et al. Electronic health record and patterns of care for children with cerebral palsy. Dev. Med. Child Neurol. 63, 1337–1343 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Chung, Y. G., Jeon, Y., Yoo, S., Kim, H. & Hwang, H. Big data analysis and artificial intelligence (AI) in epilepsy – common data model analysis and machine learning-based seizure detection and forecasting. Clin. Exp. Pediatr. 65, 272–282, https://doi.org/10.3345/CEP.2021.00766 (2022).

  65. Rogerson, S., Crowley, P. A., Crowley, S., Kohane, I. & Chou, J. The Harvard Necrotizing Enterocolitis Database: an artificial intelligence-friendly data repository with a unique ontology. J. Pediatr. Gastroenterol. Nutr. 67, S146 (2018).

    Google Scholar 

  66. Colman, R. J., Dhaliwal, J. & Rosen, M. J. Predicting therapeutic response in pediatric ulcerative colitis—a journey towards precision medicine. Front. Pediatr. 9, 19 (2021).

    Article  Google Scholar 

  67. Patel, D. et al. Does machine learning have a role in the prediction of asthma in children? Paediatr. Respir. Rev. 41, 51–60 (2022).

  68. Jackson, N. et al. Machine learning analysis of airway transcriptomic data identifies distinct childhood asthma endotypes. Am. Thorac. Soc. Int. Conf. Meet. Abstr. A1151–A1151 (2021).

  69. Oliverio, A. L. et al. Validating a computable phenotype for nephrotic syndrome in children and adults using PCORnet data. Kidney360 2, 1979–1986 (1979).

  70. Ranchin, B., Maucort-Boulch, D. & Bacchetta, J. Big data and outcomes in paediatric haemodialysis: how can nephrologists use these new tools in daily practice? Nephrol. Dial. Transplant. 36, 387–391 (2021).

    Article  PubMed  Google Scholar 

  71. McKinney, E. F., Lee, J. C., Jayne, D. R. W., Lyons, P. A. & Smith, K. G. C. T-cell exhaustion, co-stimulation and clinical outcome in autoimmunity and infection. Nature 523, 612–616 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Zhao, M., Xu, S., Cavagnaro, M. J., Zhang, W. & Shi, J. Quantitative analysis and visualization of the interaction between intestinal microbiota and type 1 diabetes in children based on multi-databases. Front. Pediatr. 9, 1389 (2021).

    Article  Google Scholar 

  73. Webb-Robertson, B. J. M. et al. Prediction of the development of islet autoantibodies through integration of environmental, genetic, and metabolic markers. J. Diabetes 13, 143–153 (2021).

    Article  CAS  PubMed  Google Scholar 

  74. Tayefi, M. et al. Challenges and opportunities beyond structured data in analysis of electronic health records. Wiley Interdiscip. Rev. Comput. Stat. 13, e1549 (2021).

    Article  Google Scholar 

  75. Househ, M. & Aldosari, B. The hazards of data mining in healthcare. Stud. Health Technol. Inform. 238, 80–83 (2017).

    PubMed  Google Scholar 

  76. Kruse, C. S., Goswamy, R., Raval, Y. & Marawi, S. Challenges and opportunities of big data in health care: a systematic review. JMIR Med. Inform. 4, e38, https://doi.org/10.2196/MEDINFORM.5359 (2016).

  77. Househ, M. S., Aldosari, B., Alanazi, A., Kushniruk, A. W. & Borycki, E. M. Big data, big problems: a healthcare perspective. Stud. Health Technol. Inform. 238, 36–39 (2017).

    PubMed  Google Scholar 

  78. Kayaalp, M. Patient privacy in the era of big data. Balk. Med. J. 35, 8–17 (2018).

    Article  Google Scholar 

  79. McCafferty, C., Chaaban, J. & Ignjatovic, V. Plasma proteomics and the paediatric patient. Expert Rev. Proteomics 16, 401–411, https://doi.org/10.1080/14789450.2019.1608186 (2019).

  80. Bardanzellu, F. & Fanos, V. How could metabolomics change pediatric health? Fanos Ital. J. Pediatr. 46, 37 (2020).

    Article  PubMed  Google Scholar 

  81. Lalonde, E. et al. Genomic diagnosis for pediatric disorders: revolution and evolution. Front. Pediatr. 8, 373 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors acknowledge Chiranjeet, at AIIMS Kalyani.

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K.M. and S.C.M. together conceptualized and designed the article. K.M. helped in acquisition of data and interpretation of data. S.C.M. drafted the article and K.M. revised it critically for important intellectual content. S.C.M. and K.M. approved the final version.

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Correspondence to Saheli Chatterjee Misra.

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Misra, S.C., Mukhopadhyay, K. Data harnessing to nurture the human mind for a tailored approach to the child. Pediatr Res (2022). https://doi.org/10.1038/s41390-022-02320-4

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