Machine Learning-Based Prediction of Hemoglobinopathies Using Complete Blood Count Data.

Anoeska SchipperMatthieu RuttenAdriaan van GammerenCornelis L HarteveldEloísa UrrechagaFloor WeerkampGijs den BestenJohannes KrabbeJennichjen SlompLise SchoonenMaarten BroerenMerel van WijnenMirelle J A J HuijskensTamara KoopmannBram van GinnekenRon KustersSteef Kurstjens

Published in: Clinical chemistry (2024)

Both the XGB and logistic regression model demonstrate high accuracy in predicting a broad range of hemoglobinopathies and are effective in differentiating hemoglobinopathies from IDA. Integration of these models into the laboratory information system facilitates automated hemoglobinopathy detection using routine CBC parameters.

Keyphrases

machine learning
big data
deep learning
artificial intelligence
electronic health record
clinical practice
high throughput
health information
healthcare
peripheral blood
magnetic resonance imaging
single cell