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Automatic blood glucose prediction with confidence using recurrent neural networks
Chalmers University of Technology, Sweden.
University of Gothenburg, Sweden.
Sahlgrenska University Hospital, Sweden.
Chalmers University of Technology, Sweden.
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2018 (English)In: CEUR Workshop Proceedings, 2018, p. 64-68Conference paper, Published paper (Refereed)
Abstract [en]

Low-cost sensors continuously measuring blood glucose levels in intervals of a few minutes and mobile platforms combined with machine-learning (ML) solutions enable personalized precision health and disease management. ML solutions must be adapted to different sensor technologies, analysis tasks and individuals. This raises the issue of scale for creating such adapted ML solutions. We present an approach for predicting blood glucose levels for diabetics up to one hour into the future. The approach is based on recurrent neural networks trained in an end-to-end fashion, requiring nothing but the glucose level history for the patient. The model outputs the prediction along with an estimate of its certainty, helping users to interpret the predicted levels. The approach needs no feature engineering or data pre-processing, and is computationally inexpensive.

Place, publisher, year, edition, pages
2018. p. 64-68
Keywords [en]
Blood, Data handling, Forecasting, Glucose, Health care, Learning systems, Blood glucose level, Data preprocessing, Disease management, Feature engineerings, Low-cost sensors, Mobile platform, Model outputs, Sensor technologies, Recurrent neural networks
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-35908Scopus ID: 2-s2.0-85051031690OAI: oai:DiVA.org:ri-35908DiVA, id: diva2:1261470
Conference
3rd International Workshop on Knowledge Discovery in Healthcare Data, KDH@IJCAI-ECAI 2018, 13 July 2018
Available from: 2018-11-07 Created: 2018-11-07 Last updated: 2019-02-04Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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