Literature DB >> 36180585

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

Saheli Chatterjee Misra1, Kaushik Mukhopadhyay2.   

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.
© 2022. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.

Entities:  

Year:  2022        PMID: 36180585     DOI: 10.1038/s41390-022-02320-4

Source DB:  PubMed          Journal:  Pediatr Res        ISSN: 0031-3998            Impact factor:   3.953


  42 in total

Review 1.  Monitoring Big Data During Mechanical Ventilation in the ICU.

Authors:  Craig D Smallwood
Journal:  Respir Care       Date:  2020-06       Impact factor: 2.258

Review 2.  Big Data and Predictive Analytics: Applications in the Care of Children.

Authors:  Srinivasan Suresh
Journal:  Pediatr Clin North Am       Date:  2016-04       Impact factor: 3.278

3.  Diagnostic Classification of ADHD Versus Control: Support Vector Machine Classification Using Brief Neuropsychological Assessment.

Authors:  Jesse C Bledsoe; Cao Xiao; Art Chaovalitwongse; Sonya Mehta; Thomas J Grabowski; Margaret Semrud-Clikeman; Steven Pliszka; David Breiger
Journal:  J Atten Disord       Date:  2016-05-26       Impact factor: 3.256

4.  Applying machine learning to facilitate autism diagnostics: pitfalls and promises.

Authors:  Daniel Bone; Matthew S Goodwin; Matthew P Black; Chi-Chun Lee; Kartik Audhkhasi; Shrikanth Narayanan
Journal:  J Autism Dev Disord       Date:  2015-05

5.  Prediction of early childhood obesity with machine learning and electronic health record data.

Authors:  Xueqin Pang; Christopher B Forrest; Félice Lê-Scherban; Aaron J Masino
Journal:  Int J Med Inform       Date:  2021-04-09       Impact factor: 4.046

6.  Childhood obesity: a growing pandemic.

Authors: 
Journal:  Lancet Diabetes Endocrinol       Date:  2021-12-02       Impact factor: 32.069

7.  Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data.

Authors:  Aaron J Masino; Mary Catherine Harris; Daniel Forsyth; Svetlana Ostapenko; Lakshmi Srinivasan; Christopher P Bonafide; Fran Balamuth; Melissa Schmatz; Robert W Grundmeier
Journal:  PLoS One       Date:  2019-02-22       Impact factor: 3.240

8.  Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data.

Authors:  Jiarui Feng; Jennifer Lee; Zachary A Vesoulis; Fuhai Li
Journal:  NPJ Digit Med       Date:  2021-07-14

9.  Predicting Hemodynamic Failure Development in PICU Using Machine Learning Techniques.

Authors:  Rosanna I Comoretto; Danila Azzolina; Angela Amigoni; Giorgia Stoppa; Federica Todino; Andrea Wolfler; Dario Gregori
Journal:  Diagnostics (Basel)       Date:  2021-07-20
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