Data Science for Child Health

Bennett, Tellen D.; Callahan, Tiffany J.; Feinstein, James A.; Ghosh, Debashis; Lakhani, Saquib A.; Spaeder, Michael C.; Szefler, Stanley J.; Kahn, Michael G.

doi:10.1016/j.jpeds.2018.12.041

Cited by 22 publications

(19 citation statements)

References 148 publications

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“…Many other applications are possible, like analyzing and predicting comorbidities through statistical network analysis [ 84 , 85 , 86 ], phenotyping, diagnosing, pharmacoepidemiology and pharmacovigilance, etc. [ 87 ].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review

Colmenarejo

2020

Nutrients

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The prevalence of childhood and adolescence overweight an obesity is raising at an alarming rate in many countries. This poses a serious threat to the current and near-future health systems, given the association of these conditions with different comorbidities (cardiovascular diseases, type II diabetes, and metabolic syndrome) and even death. In order to design appropriate strategies for its prevention, as well as understand its origins, the development of predictive models for childhood/adolescent overweight/obesity and related outcomes is of extreme value. Obesity has a complex etiology, and in the case of childhood and adolescence obesity, this etiology includes also specific factors like (pre)-gestational ones; weaning; and the huge anthropometric, metabolic, and hormonal changes that during this period the body suffers. In this way, Machine Learning models are becoming extremely useful tools in this area, given their excellent predictive power; ability to model complex, nonlinear relationships between variables; and capacity to deal with high-dimensional data typical in this area. This is especially important given the recent appearance of large repositories of Electronic Health Records (EHR) that allow the development of models using datasets with many instances and predictor variables, from which Deep Learning variants can generate extremely accurate predictions. In the current work, the area of Machine Learning models to predict childhood and adolescent obesity and related outcomes is comprehensively and critically reviewed, including the latest ones using Deep Learning with EHR. These models are compared with the traditional statistical ones that used mainly logistic regression. The main features and applications appearing from these models are described, and the future opportunities are discussed.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review

Colmenarejo

2020

Nutrients

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“…Traditional epidemiological studies often require long-term data collection efforts. The accumulated clinical practice big data in recent years provides an opportunity to glean new actionable knowledge, especially knowledge concerning children’s health 20,21 . To explore the age distribution and seasonality and co-occurrence patterns in pediatrics, we analyzed all first outpatient visits (n = 5 447 202) to our children’s hospital over a 4-year period (2013–2016).…”

Section: Introductionmentioning

confidence: 99%

PedMap: a pediatric diseases map generated from clinical big data from Hangzhou, China

Dong

et al. 2019

Sci Rep

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Epidemiological knowledge of pediatric diseases may improve professionals’ understanding of the pathophysiology of and risk factors for diseases and is also crucial for decision making related to workforce and resource planning in pediatric departments. In this study, a pediatric disease epidemiology knowledgebase called PedMap (http://pedmap.nbscn.org) was constructed from the clinical data from 5 447 202 outpatient visits of 2 189 868 unique patients at a children’s hospital (Hangzhou, China) from 2013 to 2016. The top 100 most-reported pediatric diseases were identified and visualized. These common pediatric diseases were clustered into 4 age groups and 4 seasons. The prevalence, age distribution and co-occurrence diseases for each disease were also visualized. Furthermore, an online prediction tool based on Gaussian regression models was developed to predict pediatric disease incidence based on weather information. PedMap is the first comprehensive epidemiological resource to show the full view of age-related, seasonal, climate-related variations in and co-occurrence patterns of pediatric diseases.

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“…Recent advances in high-performance computing create possibilities for the combination of data across a vast number of sources to make sense of the complex, interdependent factors that shape health. As a result of these technological advances, as with genomic medicine two decades ago, the potential for ML-based methods to solve complex disease questions has emerged [4,5]. While ML can identify important patterns that may not be extractable by the human mind, theoretical and methodological grounding is necessary to avoid misattributing causation to spurious associations.…”

Section: Matching Theory To Methods: Complex Systems Science As a Metmentioning

confidence: 99%

Charting the life course: Emerging opportunities to advance scientific approaches using life course research

Hanson

Leiser

Bandoli

et al. 2020

J. Clin. Trans. Sci.

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Life course research embraces the complexity of health and disease development, tackling the extensive interactions between genetics and environment. This interdisciplinary blueprint, or theoretical framework, offers a structure for research ideas and specifies relationships between related factors. Traditionally, methodological approaches attempt to reduce the complexity of these dynamic interactions and decompose health into component parts, ignoring the complex reciprocal interaction of factors that shape health over time. New methods that match the epistemological foundation of the life course framework are needed to fully explore adaptive, multilevel, and reciprocal interactions between individuals and their environment. The focus of this article is to (1) delineate the differences between lifespan and life course research, (2) articulate the importance of complex systems science as a methodological framework in the life course research toolbox to guide our research questions, (3) raise key questions that can be asked within the clinical and translational science domain utilizing this framework, and (4) provide recommendations for life course research implementation, charting the way forward. Recent advances in computational analytics, computer science, and data collection could be used to approximate, measure, and analyze the intertwining and dynamic nature of genetic and environmental factors involved in health development.

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Data Science for Child Health

Cited by 22 publications

References 148 publications

Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review

Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review

PedMap: a pediatric diseases map generated from clinical big data from Hangzhou, China

Charting the life course: Emerging opportunities to advance scientific approaches using life course research

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