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Glucose TSA

This workflow uses sensor data from a wearable glucose monitoring device to generate 30 minute forecasts for glucose levels, then using a rule engine sets output message as "Glucose level is too high", "Glucose level is too low", or "All is well".

A Scatter Plot visualizes the output messages over the glucose sensor log.

Build Keras Network Architecture Post processing and creatureof response rules for whenglucose is forecasted to be toohigh or too low. To Deployment Workflow Glucose Readings80% from top for trainingtraining withMSLE loss functioncombine 6 mostrecent values intotarget listlag values36 unit input3 hours6 unit outputsoftplus activation30 minutes forecastcombine 36 values before the target intoinput listcombine 36 values before the target intoinput listcombine 6 mostrecent values intotarget listlag values18 unit dense layersoftplus activation9 unit dense layersoftplus activation> 12.5 too high< 2.5 too lowcolor coderesponse messagesto: Deployment Glucose TSAstart deploymentworkflowend deploymentworkflow Table Reader Partitioning Keras NetworkLearner Column Aggregator Lag Column Keras Input Layer Keras Dense Layer Column Aggregator Keras NetworkExecutor Column Aggregator Column Aggregator Lag Column Keras Dense Layer Keras Dense Layer Rule Engine Color Manager Scatter Plot Normalizer Normalizer (Apply) Split Collectionsand Denormalize Workflow Writer CaptureWorkflow Start CaptureWorkflow End Numeric Scorer Build Keras Network Architecture Post processing and creatureof response rules for whenglucose is forecasted to be toohigh or too low. To Deployment Workflow Glucose Readings80% from top for trainingtraining withMSLE loss functioncombine 6 mostrecent values intotarget listlag values36 unit input3 hours6 unit outputsoftplus activation30 minutes forecastcombine 36 values before the target intoinput listcombine 36 values before the target intoinput listcombine 6 mostrecent values intotarget listlag values18 unit dense layersoftplus activation9 unit dense layersoftplus activation> 12.5 too high< 2.5 too lowcolor coderesponse messagesto: Deployment Glucose TSAstart deploymentworkflowend deploymentworkflow Table Reader Partitioning Keras NetworkLearner Column Aggregator Lag Column Keras Input Layer Keras Dense Layer Column Aggregator Keras NetworkExecutor Column Aggregator Column Aggregator Lag Column Keras Dense Layer Keras Dense Layer Rule Engine Color Manager Scatter Plot Normalizer Normalizer (Apply) Split Collectionsand Denormalize Workflow Writer CaptureWorkflow Start CaptureWorkflow End Numeric Scorer

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: corey
Created at: 2022-04-12
On NodePit since: 2022-04-13
Last update: 2024-04-27
Created with KNIME version: v4.6.0
Tags: KerasForecastingSensorIoTNeural NetworkDeep LearningTime SeriesTSAData Science

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