This workflow reads in the diabetes subset from the Dubosson data set, which contains glucose measurements, as well as several sensor measurements like heartrate, breathing rate, activity and acceleration. The exploration Component generates an overview of all the different measurements and lets the user filter the data by date and patient. To update the view without closing the window, the "Refresh Button" can be used. Afterwards an LSTM network is trained using 15 minutes of blood glucose measurements. The goal is to predict the next 15 minutes of blood glucose levels for each patient. The data is split into training and test set by taking 80% of the data for each patient from the top as training set and the remaining 20% as test set. "Diabetes_Patient_008" is not included in the training/testing process. This patient will be used for validation purposes. At the end, the model performance is displayed in an interactive view. In this view different Patient IDs can be selected and the corresponding Line Plot, as well as the RMSE score is shown. Also the results for "Diabetes_Patient_008", which is used for validation, is shown via a Line Plot.
This is a companion discussion topic for the original entry at https://kni.me/w/RW5UCM5iRIAZpZqv