AIMC Topic: Human Growth Hormone

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Effects of gender, body weight, and blood glucose dynamics on the growth hormone response to the glucagon stimulation test in patients with pituitary disease.

Growth hormone & IGF research : official journal of the Growth Hormone Research Society and the International IGF Research Society
OBJECTIVE: Body weight blunts the growth hormone (GH) response to provocative stimuli. The appropriate GH cut-off to confirm GH deficiency in obese and overweight patients undergoing the glucagon stimulation test (GST) has recently been questioned. W...

Machine Learning-based Prediction Model for Treatment of Acromegaly With First-generation Somatostatin Receptor Ligands.

The Journal of clinical endocrinology and metabolism
CONTEXT: Artificial intelligence (AI), in particular machine learning (ML), may be used to deeply analyze biomarkers of response to first-generation somatostatin receptor ligands (fg-SRLs) in the treatment of acromegaly.

Using Deep Learning for Individual-Level Predictions of Adherence with Growth Hormone Therapy.

Studies in health technology and informatics
The problem of consistent therapy adherence is a current challenge for health informatics, and its solution can increase the success rate of treatments. Here we show a methodology to predict, at individual-level, future therapy adherence for patients...

Machine learning-based prediction of response to growth hormone treatment in Turner syndrome: the LG Growth Study.

Journal of pediatric endocrinology & metabolism : JPEM
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 ...

Neural network models - a novel tool for predicting the efficacy of growth hormone (GH) therapy in children with short stature.

Neuro endocrinology letters
INTRODUCTION: The leading method for prediction of growth hormone (GH) therapy effectiveness are multiple linear regression (MLR) models. Best of our knowledge, we are the first to apply artificial neural networks (ANN) to solve this problem. For ANN...