AIMC Topic: Growth Disorders

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Sociodemographic risk factors of under-five stunting in Bangladesh: Assessing the role of interactions using a machine learning method.

PloS one Aug 31, 2021
This paper aims to demonstrate the importance of studying interactions among various sociodemographic risk factors of childhood stunting in Bangladesh with the help of an interpretable machine learning method. Data used for the analyses are extracted...
Child, Preschool Logistic Models Sociodemographic Factors Bangladesh Male Infant Growth Disorders Odds Ratio Humans Machine Learning Female Infant, Newborn Reproducibility of Results Risk Factors
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Digital technologies to improve the precision of paediatric growth disorder diagnosis and management.

Growth hormone & IGF research : official journal of the Growth Hormone Research Society and the International IGF Research Society Jun 4, 2021
Paediatric disorders of impaired linear growth are challenging to manage, in part because of delays in the identification of pathological short stature and subsequent referral and diagnosis, the requirement for long-term therapy, and frequent poor ad...
Digital Technology Humans Assessment of Medication Adherence Artificial Intelligence Human Growth Hormone Growth Disorders
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Model and variable selection using machine learning methods with applications to childhood stunting in Bangladesh.

Informatics for health & social care Apr 14, 2021
Childhood stunting is a serious public health concern in Bangladesh. Earlier research used conventional statistical methods to identify the risk factors of stunting, and very little is known about the applications and usefulness of machine learning (...
Risk Factors Aged Infant Growth Disorders Humans Machine Learning Child Logistic Models Bangladesh
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Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth - a four-year prospective study.

BMC pediatrics Oct 30, 2020
BACKGROUND: Stunting affects up to one-third of the children in low-to-middle income countries (LMICs) and has been correlated with decline in cognitive capacity and vaccine immunogenicity. Early identification of infants at risk is critical for earl...
Pakistan Infant Growth Disorders Child, Preschool Child Pregnancy Female Infant, Newborn Humans Machine Learning Prospective Studies Biomarkers
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Quality of Life in Adolescent Boys with Idiopathic Short Stature: Positive Impact of Growth Hormone and Aromatase Inhibitors.

Hormone research in paediatrics Feb 28, 2019
BACKGROUND: The combination of growth hormone (GH) and aromatase inhibitors (AI) improves linear growth in severely short adolescent boys; however, the effects of this intervention on quality of life (QoL) are unknown. This study assesses whether GH,...
Follow-Up Studies Surveys and Questionnaires Aromatase Inhibitors Humans Adolescent Male Human Growth Hormone Quality of Life Growth Disorders
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Transcriptomics and machine learning predict diagnosis and severity of growth hormone deficiency.

JCI insight Apr 5, 2018
BACKGROUND: The effect of gene expression data on diagnosis remains limited. Here, we show how diagnosis and classification of growth hormone deficiency (GHD) can be achieved from a single blood sample using a combination of transcriptomics and rando...
Gene Regulatory Networks Polymorphism, Single Nucleotide Predictive Value of Tests Principal Component Analysis Healthy Volunteers Datasets as Topic Growth Disorders Child, Preschool Child ROC Curve Case-Control Studies Adolescent Human Growth Hormone Humans Machine Learning Male Biomarkers Severity of Illness Index Female Gene Expression Profiling Transcriptome
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Evaluating Prevalence of Preterm Postnatal Growth Faltering Using Fenton 2013 and INTERGROWTH-21st Growth Charts with Logistic and Machine Learning Models.

Nutrients May 20, 2025
Postnatal growth faltering (PGF) significantly affects premature neonates, leading to compromised neurodevelopment and an increased risk of long-term health complications. This retrospective study at a level III NICU of a tertiary hospital analyzed...
Machine Learning Retrospective Studies Female Infant, Newborn Humans Infant, Premature Gestational Age Infant, Small for Gestational Age Growth Charts Male Prevalence Growth Disorders Logistic Models
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Explainable predictive models of short stature and exploration of related environmental growth factors: a case-control study.

BMC endocrine disorders May 12, 2025
BACKGROUND: Short stature is a prevalent pediatric endocrine disorder for which early detection and prediction are pivotal for improving treatment outcomes. However, existing diagnostic criteria often lack the necessary sensitivity and specificity be...
Child, Preschool Humans Machine Learning Child Case-Control Studies Prognosis Body Height Adolescent Female Growth Disorders Male
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Application of Machine Learning Approaches to Predict Postnatal Growth Failure in Very Low Birth Weight Infants.

Yonsei medical journal Jul 1, 2022
PURPOSE: The aims of the study were to develop and evaluate a machine learning model with which to predict postnatal growth failure (PGF) among very low birth weight (VLBW) infants.
Support Vector Machine Infant, Very Low Birth Weight Humans Machine Learning Infant, Newborn Infant Growth Disorders
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Machine learning-based prediction of response to growth hormone treatment in Turner syndrome: the LG Growth Study.

Journal of pediatric endocrinology & metabolism : JPEM Jan 28, 2020
Background Growth hormone (GH) treatment has become a common practice in Turner syndrome (TS). However, there are only a few studies on the response to GH treatment in TS. The aim of this study is to predict the responsiveness to GH treatment and to ...
Humans Child Predictive Value of Tests Body Height Turner Syndrome Male Machine Learning Human Growth Hormone Retrospective Studies Time Factors Female Growth Disorders
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