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

Matheus Araujo, Paula van Dommelen, Ekaterina Koledova, Jaideep Srivastava

Research output: Contribution to journalArticlepeer-review

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 (<85%) periods. When evaluated with thousands of therapy segments extracted from a test set, our model reached an AUC-PR score of 0.79 and AUC-ROC of 0.90; both metrics were consistently better than traditional approaches, such as simple average model. Using this model, we can perform precise early identification of patients who are likely to become non-adherent patients. This opens a path for healthcare practitioners to personalize GH therapy at any stage of the patients' journey and improve shared decision making with patients and caregivers to achieve optimal outcomes.

Original languageEnglish (US)
Pages (from-to)133-137
Number of pages5
JournalStudies in health technology and informatics
Volume281
DOIs
StatePublished - May 27 2021

Keywords

  • Deep learning
  • growth hormone therapy
  • therapy adherence

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