Meal and Physical Activity Detection from Free-living Data for Discovering Disturbance Patterns to Glucose Levels in People with Diabetes.
Mohammad Reza AskariMudassir RashidXiaoyu SunMert SevilAndrew ShahidehpourKeigo KawajiAli CinarPublished in: BioMedInformatics (2022)
RNNs with LSTM and 1D convolution layers and bidirectional LSTM with 1D convolution layers provide accurate personalized information about the daily routines of individuals. Significance: Capturing daily behavior patterns enables more accurate future BGC predictions in AID systems and improves BGC regulation.
Keyphrases
- neural network
- physical activity
- type diabetes
- high resolution
- cardiovascular disease
- body mass index
- glycemic control
- current status
- blood glucose
- solar cells
- loop mediated isothermal amplification
- electronic health record
- health information
- big data
- real time pcr
- machine learning
- label free
- sleep quality
- weight loss
- social media