TY - GEN
T1 - Population and Individual Level Meal Response Patterns in Continuous Glucose Data
AU - de Carvalho, Danilo Ferreira
AU - Kaymak, Uzay
AU - van Gorp, Pieter
AU - van Riel, Natal
N1 - Funding Information: Acknowledgment. This publication is part of the project DiaGame (with project number 628.011.027) of the research programme Data2Person which is (partly) financed by the Dutch Research Council (NWO). Publisher Copyright: © 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Diabetes research has changed with the introduction of wearables that are able to continuously collect physiological data (e.g., blood glucose levels), which has allowed for data-driven solutions. In this context, patients are still expected to self-record events tied to their daily routines (e.g., meals). Since self-recording is prone to errors, automatic detection of meal events could improve the quality of event data and reduce registration burden. In this paper, we investigate the feasibility of meal detection from continuous glucose data by using population level data compared to individual data. We discuss the advantages and disadvantages of both approaches based on a method to identify patterns in time series that can be used to map the characteristics of a glucose signal response to a meal event. Event responses, i.e., subsequences that come right after a recorded event, are identified and fuzzy clustering is used to group different types of them. Our results indicate that both population and individual data give comparable results, which suggests that both could be used interchangeably to develop event identification models.
AB - Diabetes research has changed with the introduction of wearables that are able to continuously collect physiological data (e.g., blood glucose levels), which has allowed for data-driven solutions. In this context, patients are still expected to self-record events tied to their daily routines (e.g., meals). Since self-recording is prone to errors, automatic detection of meal events could improve the quality of event data and reduce registration burden. In this paper, we investigate the feasibility of meal detection from continuous glucose data by using population level data compared to individual data. We discuss the advantages and disadvantages of both approaches based on a method to identify patterns in time series that can be used to map the characteristics of a glucose signal response to a meal event. Event responses, i.e., subsequences that come right after a recorded event, are identified and fuzzy clustering is used to group different types of them. Our results indicate that both population and individual data give comparable results, which suggests that both could be used interchangeably to develop event identification models.
KW - Continuous glucose data
KW - Distance profile
KW - Fuzzy clustering
KW - Meal detection
KW - Pattern identification
UR - http://www.scopus.com/inward/record.url?scp=85135012390&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-031-08974-9_19
DO - https://doi.org/10.1007/978-3-031-08974-9_19
M3 - Conference contribution
SN - 9783031089732
VL - 1602 CCIS
T3 - Communications in Computer and Information Science
SP - 235
EP - 247
BT - Information Processing and Management of Uncertainty in Knowledge-Based Systems - 19th International Conference, IPMU 2022, Proceedings
A2 - Ciucci, Davide
A2 - Couso, Inés
A2 - Medina, Jesús
A2 - Ślęzak, Dominik
A2 - Petturiti, Davide
A2 - Bouchon-Meunier, Bernadette
A2 - Yager, Ronald R.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2022
Y2 - 11 July 2022 through 15 July 2022
ER -