English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The adoption of deep learning in healthcare is hindered by their "black box"nature. In this paper, we explore the RETAIN architecture for the task ofglusose forecasting for diabetic people. By using a two-level attentionmechanism, the recurrent-neural-network-based RETAIN model is interpretable. Weevaluate the RETAIN model on the type-2 IDIAB and the type-1 OhioT1DM datasetsby comparing its statistical and clinical performances against two deep modelsand three models based on decision trees. We show that the RETAIN model offersa very good compromise between accuracy and interpretability, being almost asaccurate as the LSTM and FCN models while remaining interpretable. We show theusefulness of its interpretable nature by analyzing the contribution of eachvariable to the final prediction. It revealed that signal values older than onehour are not used by the RETAIN model for the 30-minutes ahead of timeprediction of glucose. Also, we show how the RETAIN model changes its behaviorupon the arrival of an event such as carbohydrate intakes or insulin infusions.In particular, it showed that the patient's state before the event isparticularily important for the prediction. Overall the RETAIN model, thanks toits interpretability, seems to be a very promissing model for regression orclassification tasks in healthcare.

Enhancing the Interpretability of Deep Models in Heathcare Through Attention: Application to Glucose Forecasting for Diabetic People