Title
Using Deep Learning for Individual-Level Predictions of Adherence with Growth Hormone Therapy
Author
Araujo, M.
van Dommelen, P.
Koledova, E.B.
Srivastava, J.
Publication year
2021
Abstract
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 receiving daily injections of growth hormone (GH) therapy for GH deficiency. Our proposed model is able to generate predictions of future adherence using a recurrent neural network with adherence data recorded by easypodTM, a connected autoinjection device. The model was trained with a multi-year long dataset with 2500 patients, from January 2007 to June 2019. When testing, the model reached an average sensitivity of 0.70 and a specificity of 0.88 per patient when predicting non-adherence (
Subject
Deep learning
Growth hormone therapy
Therapy adherence
To reference this document use:
http://resolver.tudelft.nl/uuid:66cf2fb2-30f0-4a6e-b19a-6e6169213f8b
DOI
https://doi.org/10.3233/shti210135
TNO identifier
956401
ISBN
9781643681856 ; 9781643681849
Source
Studies in Health Technology and Informatics, 281 (281), 133-137
Document type
article