Using Deep Learning for Individual-Level Predictions of Adherence with Growth Hormone Therapy
article
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. Chemicals / CAS growth hormone, 36992-73-1, 37267-05-3, 66419-50-9, 9002-72-6; human growth hormone, 12629-01-5; Growth Hormone; Human Growth Hormone.
TNO Identifier
956401
ISBN
9781643681856 ; 9781643681849
Source
Studies in Health Technology and Informatics, 281, pp. 133-137.
Pages
133-137