Dataset for sample T1DM simulated patients used to validate the paper Muñoz-Organero, M. “Deep physiological model for blood glucose prediction in T1DM patients”. Sensors (Switzerland), 2020, 20(14), pp. 1–17, 3896

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Muñoz Organero, Mario, 2024, "Dataset for sample T1DM simulated patients used to validate the paper Muñoz-Organero, M. “Deep physiological model for blood glucose prediction in T1DM patients”. Sensors (Switzerland), 2020, 20(14), pp. 1–17, 3896", https://doi.org/10.21950/MMVBUJ, e-cienciaDatos, V1

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Description	The AIDA diabetes simulator has been used to generate 10 days of data for the different models implemented by the tool.
Subject	Engineering; Medicine, Health and Life Sciences
Keyword	Blood glucose prediction, type 1 diabetes mellitus, deep machine learning, physiological models
Related Publication	Muñoz-Organero, M. (2020). Deep physiological model for blood glucose prediction in T1DM patients. Sensors, 20(14), pp. 1–17, 3896,doi: 10.3390/s20143896
Notes	The dataset could help reproducing the results in the companiog paper and to foster new research. Description of the project: The dataset has been gathered to valitade the research presented in Munoz-Organero, M. “Deep physiological model for blood glucose prediction in T1DM patients”. Sensors (Switzerland), 2020, 20(14), pp. 1–17, 3896 A new mechanism inspired by metabolic models for glucose dynamics and trainable on a per-patient-basis is proposed. The differential equations for carbohydrate and insulin absorption are modeled using a Recurrent Neural Network (RNN) implemented using Long Short-Term Memory (LSTM) cells. Methodology: The dataset has been generated using the AIDA diabetes simulator which is intended for simulating the effects on the blood glucose profile of changes in insulin and diet for a typical insulin-dependent (type 1) diabetic patient. The simulator includes 40 different patient models with different parameters controlling the metabolic model that is used in order to generate BG levels for different food intake and insulin injection patterns. The AIDA diabetes simulator can be downloaded as a freeware tool or it can be used online. The simulator uses a 15 min sample rate for simulated BG levels.
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	sample-T1DM_munoz-organero_2023_v01_readme.txt Plain Text - 4,2 KB Published Jan 8, 2024 4 Downloads MD5: f713376f76a4607d266886d1e69946ed DocumentaciónDocumentation	Preview "sample-T1DM_munoz-organero_2023_v01_readme.txt" Access File File Access Public Download Options Plain Text Download Metadata Data File Citation EndNote XML RIS BibTeX Explore Options Read Text
	p1.tab AIDA_patients/Tabular Data - 100,8 KB Published Jan 8, 2024 8 Downloads 1 Variables, 3072 Observations DataDatos	Preview "AIDA_patients/p1.tab" Access File File Access Public Download Options Comma Separated Values (Original File Format) Tab-Delimited RData Download Metadata DDI Codebook v2 Data File Citation EndNote XML RIS BibTeX Explore Options View Data
	p2.tab AIDA_patients/Tabular Data - 16,3 KB Published Jan 8, 2024 3 Downloads 1 Variables, 768 Observations DataDatos	Preview "AIDA_patients/p2.tab" Access File File Access Public Download Options Comma Separated Values (Original File Format) Tab-Delimited RData Download Metadata DDI Codebook v2 Data File Citation EndNote XML RIS BibTeX Explore Options View Data
	p3.tab AIDA_patients/Tabular Data - 20,0 KB Published Jan 8, 2024 3 Downloads 1 Variables, 768 Observations DataDatos	Preview "AIDA_patients/p3.tab" Access File File Access Public Download Options Comma Separated Values (Original File Format) Tab-Delimited RData Download Metadata DDI Codebook v2 Data File Citation EndNote XML RIS BibTeX Explore Options View Data
	p4.tab AIDA_patients/Tabular Data - 14,2 KB Published Jan 8, 2024 3 Downloads 1 Variables, 673 Observations DataDatos	Preview "AIDA_patients/p4.tab" Access File File Access Public Download Options Comma Separated Values (Original File Format) Tab-Delimited RData Download Metadata DDI Codebook v2 Data File Citation EndNote XML RIS BibTeX Explore Options View Data

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Persistent Identifier	doi:10.21950/MMVBUJ
Publication Date	2024-01-08
Title	Dataset for sample T1DM simulated patients used to validate the paper Muñoz-Organero, M. “Deep physiological model for blood glucose prediction in T1DM patients”. Sensors (Switzerland), 2020, 20(14), pp. 1–17, 3896
Author	Universidad Carlos III de Madrid0000-0003-4199-2002
Point of Contact	Use email button above to contact. Muñoz Organero, Mario (Universidad Carlos III de Madrid)
Description	The AIDA diabetes simulator has been used to generate 10 days of data for the different models implemented by the tool.
Subject	Engineering; Medicine, Health and Life Sciences
Keyword	Blood glucose prediction type 1 diabetes mellitus deep machine learning physiological models
Related Publication	Muñoz-Organero, M. (2020). Deep physiological model for blood glucose prediction in T1DM patients. Sensors, 20(14), pp. 1–17, 3896, doi 10.3390/s20143896 https://doi.org/10.3390/s20143896
Notes	The dataset could help reproducing the results in the companiog paper and to foster new research. Description of the project: The dataset has been gathered to valitade the research presented in Munoz-Organero, M. “Deep physiological model for blood glucose prediction in T1DM patients”. Sensors (Switzerland), 2020, 20(14), pp. 1–17, 3896 A new mechanism inspired by metabolic models for glucose dynamics and trainable on a per-patient-basis is proposed. The differential equations for carbohydrate and insulin absorption are modeled using a Recurrent Neural Network (RNN) implemented using Long Short-Term Memory (LSTM) cells. Methodology: The dataset has been generated using the AIDA diabetes simulator which is intended for simulating the effects on the blood glucose profile of changes in insulin and diet for a typical insulin-dependent (type 1) diabetic patient. The simulator includes 40 different patient models with different parameters controlling the metabolic model that is used in order to generate BG levels for different food intake and insulin injection patterns. The AIDA diabetes simulator can be downloaded as a freeware tool or it can be used online. The simulator uses a 15 min sample rate for simulated BG levels.
Language	English
Funding Information	Agencia Estatal de Investigación: TIN2016-77158-C4-1-R
Depositor	Muñoz Organero, Mario
Deposit Date	2023-12-22
Date of Collection	Start Date: 2019
Software	The implemented model is described in the companion paper. In particular, the implementation of the model in Phython is described in Appendix A. Model Implementation

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