DICAT NATIONAL BIOMEDICAL COMPUTATION CENTRE

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DICAT NATIONAL BIOMEDICAL COMPUTATION CENTRE

DICAT NATIONAL BIOMEDICAL COMPUTATION CENTREDICAT NATIONAL BIOMEDICAL COMPUTATION CENTREDICAT NATIONAL BIOMEDICAL COMPUTATION CENTRE
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Endocrinology

Director

Xiao Lisa Liu, Ph.D  

Field

Bioinformatics; diabetes mellitus. 

Introduction

Artificial Intelligence (AI) and machine learning (ML) is a growing field for endocrinology and has already made inroads in the treatment of diabetes. Endocrinology is especially well positioned to take advantage of the upsurge in ML work focused on interpretability and explainability, which focus on developing models based on the trade-off between model accuracy and transparency rather than accuracy alone. Researchers in our department have demonstrated the power of an explainable ML model to predict adult height from growth measurements of children before and at the age of 6 years. In this work they not only demonstrate superior performance in the context of accuracy but utilize the explainable model to provide insight into the factors underlying that performance as well as understand the potential deficiencies in the model that could be addressed in future developments.

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