One-Day Bayesian Cloning of Type 1 Diabetes Subjects: Toward a Single-Day UVA/Padova Type 1 Diabetes Simulator

Objective : The UVA/Padova Type 1 Diabetes (T1DM) Simulator has been shown to be representative of a T1DM population observed in a clinical trial, but has not yet been identified on T1DM data. Moreover, the current version of the simulator is "single meal" while making it "single-day...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Visentin, Roberto [verfasserIn] Man, Chiara Dalla Cobelli, Claudio

Format:	Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Sugar closed-loop control Sociology in silico circadian variability Bayes methods Mathematical model Artificial pancreas Plasmas compartmental modeling Data models Insulin Glucose

Systematik:	XA 48665

Übergeordnetes Werk:	Enthalten in: IEEE transactions on biomedical engineering - New York, NY : IEEE, 1964, 63(2016), 11, Seite 2416-2424
Übergeordnetes Werk:	volume:63 ; year:2016 ; number:11 ; pages:2416-2424

Links:	Volltext Link aufrufen

DOI / URN:	10.1109/TBME.2016.2535241

Katalog-ID:	OLC1984429213

Internformat


LEADER	01000caa a2200265 4500
001	OLC1984429213
003	DE-627
005	20230508071808.0
007	tu
008	161202s2016 xx \|\|\|\|\| 00\| \|\|eng c
024	7		\|a 10.1109/TBME.2016.2535241 \|2 doi
028	5	2	\|a PQ20161201
035			\|a (DE-627)OLC1984429213
035			\|a (DE-599)GBVOLC1984429213
035			\|a (PRQ)c1651-fad7c35a1926f33d6a4a1d71bd28e4d66b33bbd67b3e947818bfe4f561e65e050
035			\|a (KEY)0037705820160000063001102416onedaybayesiancloningoftype1diabetessubjectstoward
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
082	0	4	\|a 620 \|a 610 \|q DE-600
084			\|a XA 48665 \|q AVZ \|2 rvk
084			\|a 44.09 \|2 bkl
084			\|a 44.40 \|2 bkl
100	1		\|a Visentin, Roberto \|e verfasserin \|4 aut
245	1	0	\|a One-Day Bayesian Cloning of Type 1 Diabetes Subjects: Toward a Single-Day UVA/Padova Type 1 Diabetes Simulator
264		1	\|c 2016
336			\|a Text \|b txt \|2 rdacontent
337			\|a ohne Hilfsmittel zu benutzen \|b n \|2 rdamedia
338			\|a Band \|b nc \|2 rdacarrier
520			\|a Objective : The UVA/Padova Type 1 Diabetes (T1DM) Simulator has been shown to be representative of a T1DM population observed in a clinical trial, but has not yet been identified on T1DM data. Moreover, the current version of the simulator is "single meal" while making it "single-day centric," i.e., by describing intraday variability, would be a step forward to create more realistic in silico scenarios. Here, we propose a Bayesian method for the identification of the model from plasma glucose and insulin concentrations only, by exploiting the prior model parameter distribution. Methods : The database consists of 47 T1DM subjects, who received dinner, breakfast, and lunch (respectively, 80, 50, and 60 CHO grams) in three 23-h occasions (one open- and one closed-loop). The model is identified using the Bayesian Maximum a Posteriori technique, where the prior parameter distribution is that of the simulator. Diurnal variability of glucose absorption and insulin sensitivity is allowed. Results : The model well describes glucose traces (coefficient of determination <inline-formula><tex-math notation="LaTeX">R^{2} = 0.962 \,\pm\, 0.027 </tex-math></inline-formula>) and the posterior parameter distribution is similar to that included in the simulator. Absorption parameters at breakfast are significantly different from those at lunch and dinner, reflecting more rapid dynamics of glucose absorption. Insulin sensitivity varies in each individual but without a specific pattern. Conclusion : The incorporation of glucose absorption and insulin sensitivity diurnal variability into the simulator makes it more realistic. Significance : The proposed method, applied to the increasing number of long-term artificial pancreas studies, will allow to describe week/month variability, thus further refining the simulator.
650		4	\|a Sugar
650		4	\|a closed-loop control
650		4	\|a Sociology
650		4	\|a in silico
650		4	\|a circadian variability
650		4	\|a Bayes methods
650		4	\|a Mathematical model
650		4	\|a Artificial pancreas
650		4	\|a Plasmas
650		4	\|a compartmental modeling
650		4	\|a Data models
650		4	\|a Insulin
650		4	\|a Glucose
700	1		\|a Man, Chiara Dalla \|4 oth
700	1		\|a Cobelli, Claudio \|4 oth
773	0	8	\|i Enthalten in \|t IEEE transactions on biomedical engineering \|d New York, NY : IEEE, 1964 \|g 63(2016), 11, Seite 2416-2424 \|w (DE-627)129358452 \|w (DE-600)160429-6 \|w (DE-576)01473074X \|x 0018-9294 \|7 nnns
773	1	8	\|g volume:63 \|g year:2016 \|g number:11 \|g pages:2416-2424
856	4	1	\|u http://dx.doi.org/10.1109/TBME.2016.2535241 \|3 Volltext
856	4	2	\|u http://ieeexplore.ieee.org/document/7420664
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_OLC
912			\|a SSG-OLC-TEC
912			\|a SSG-OLC-PHA
912			\|a SSG-OLC-DE-84
912			\|a SSG-OPC-PHA
912			\|a GBV_ILN_70
912			\|a GBV_ILN_170
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2410
912			\|a GBV_ILN_4219
936	r	v	\|a XA 48665
936	b	k	\|a 44.09 \|q AVZ
936	b	k	\|a 44.40 \|q AVZ
951			\|a AR
952			\|d 63 \|j 2016 \|e 11 \|h 2416-2424

Indexfelder

author_variant	r v rv
matchkey_str	article:00189294:2016----::ndyaeinlnnotp1ibtsujcsoadsnldyvpd
hierarchy_sort_str	2016
bklnumber	44.09 44.40
publishDate	2016
allfields	10.1109/TBME.2016.2535241 doi PQ20161201 (DE-627)OLC1984429213 (DE-599)GBVOLC1984429213 (PRQ)c1651-fad7c35a1926f33d6a4a1d71bd28e4d66b33bbd67b3e947818bfe4f561e65e050 (KEY)0037705820160000063001102416onedaybayesiancloningoftype1diabetessubjectstoward DE-627 ger DE-627 rakwb eng 620 610 DE-600 XA 48665 AVZ rvk 44.09 bkl 44.40 bkl Visentin, Roberto verfasserin aut One-Day Bayesian Cloning of Type 1 Diabetes Subjects: Toward a Single-Day UVA/Padova Type 1 Diabetes Simulator 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Objective : The UVA/Padova Type 1 Diabetes (T1DM) Simulator has been shown to be representative of a T1DM population observed in a clinical trial, but has not yet been identified on T1DM data. Moreover, the current version of the simulator is "single meal" while making it "single-day centric," i.e., by describing intraday variability, would be a step forward to create more realistic in silico scenarios. Here, we propose a Bayesian method for the identification of the model from plasma glucose and insulin concentrations only, by exploiting the prior model parameter distribution. Methods : The database consists of 47 T1DM subjects, who received dinner, breakfast, and lunch (respectively, 80, 50, and 60 CHO grams) in three 23-h occasions (one open- and one closed-loop). The model is identified using the Bayesian Maximum a Posteriori technique, where the prior parameter distribution is that of the simulator. Diurnal variability of glucose absorption and insulin sensitivity is allowed. Results : The model well describes glucose traces (coefficient of determination <inline-formula><tex-math notation="LaTeX">R^{2} = 0.962 \,\pm\, 0.027 </tex-math></inline-formula>) and the posterior parameter distribution is similar to that included in the simulator. Absorption parameters at breakfast are significantly different from those at lunch and dinner, reflecting more rapid dynamics of glucose absorption. Insulin sensitivity varies in each individual but without a specific pattern. Conclusion : The incorporation of glucose absorption and insulin sensitivity diurnal variability into the simulator makes it more realistic. Significance : The proposed method, applied to the increasing number of long-term artificial pancreas studies, will allow to describe week/month variability, thus further refining the simulator. Sugar closed-loop control Sociology in silico circadian variability Bayes methods Mathematical model Artificial pancreas Plasmas compartmental modeling Data models Insulin Glucose Man, Chiara Dalla oth Cobelli, Claudio oth Enthalten in IEEE transactions on biomedical engineering New York, NY : IEEE, 1964 63(2016), 11, Seite 2416-2424 (DE-627)129358452 (DE-600)160429-6 (DE-576)01473074X 0018-9294 nnns volume:63 year:2016 number:11 pages:2416-2424 http://dx.doi.org/10.1109/TBME.2016.2535241 Volltext http://ieeexplore.ieee.org/document/7420664 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-PHA GBV_ILN_70 GBV_ILN_170 GBV_ILN_2061 GBV_ILN_2410 GBV_ILN_4219 XA 48665 44.09 AVZ 44.40 AVZ AR 63 2016 11 2416-2424
spelling	10.1109/TBME.2016.2535241 doi PQ20161201 (DE-627)OLC1984429213 (DE-599)GBVOLC1984429213 (PRQ)c1651-fad7c35a1926f33d6a4a1d71bd28e4d66b33bbd67b3e947818bfe4f561e65e050 (KEY)0037705820160000063001102416onedaybayesiancloningoftype1diabetessubjectstoward DE-627 ger DE-627 rakwb eng 620 610 DE-600 XA 48665 AVZ rvk 44.09 bkl 44.40 bkl Visentin, Roberto verfasserin aut One-Day Bayesian Cloning of Type 1 Diabetes Subjects: Toward a Single-Day UVA/Padova Type 1 Diabetes Simulator 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Objective : The UVA/Padova Type 1 Diabetes (T1DM) Simulator has been shown to be representative of a T1DM population observed in a clinical trial, but has not yet been identified on T1DM data. Moreover, the current version of the simulator is "single meal" while making it "single-day centric," i.e., by describing intraday variability, would be a step forward to create more realistic in silico scenarios. Here, we propose a Bayesian method for the identification of the model from plasma glucose and insulin concentrations only, by exploiting the prior model parameter distribution. Methods : The database consists of 47 T1DM subjects, who received dinner, breakfast, and lunch (respectively, 80, 50, and 60 CHO grams) in three 23-h occasions (one open- and one closed-loop). The model is identified using the Bayesian Maximum a Posteriori technique, where the prior parameter distribution is that of the simulator. Diurnal variability of glucose absorption and insulin sensitivity is allowed. Results : The model well describes glucose traces (coefficient of determination <inline-formula><tex-math notation="LaTeX">R^{2} = 0.962 \,\pm\, 0.027 </tex-math></inline-formula>) and the posterior parameter distribution is similar to that included in the simulator. Absorption parameters at breakfast are significantly different from those at lunch and dinner, reflecting more rapid dynamics of glucose absorption. Insulin sensitivity varies in each individual but without a specific pattern. Conclusion : The incorporation of glucose absorption and insulin sensitivity diurnal variability into the simulator makes it more realistic. Significance : The proposed method, applied to the increasing number of long-term artificial pancreas studies, will allow to describe week/month variability, thus further refining the simulator. Sugar closed-loop control Sociology in silico circadian variability Bayes methods Mathematical model Artificial pancreas Plasmas compartmental modeling Data models Insulin Glucose Man, Chiara Dalla oth Cobelli, Claudio oth Enthalten in IEEE transactions on biomedical engineering New York, NY : IEEE, 1964 63(2016), 11, Seite 2416-2424 (DE-627)129358452 (DE-600)160429-6 (DE-576)01473074X 0018-9294 nnns volume:63 year:2016 number:11 pages:2416-2424 http://dx.doi.org/10.1109/TBME.2016.2535241 Volltext http://ieeexplore.ieee.org/document/7420664 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-PHA GBV_ILN_70 GBV_ILN_170 GBV_ILN_2061 GBV_ILN_2410 GBV_ILN_4219 XA 48665 44.09 AVZ 44.40 AVZ AR 63 2016 11 2416-2424
allfields_unstemmed	10.1109/TBME.2016.2535241 doi PQ20161201 (DE-627)OLC1984429213 (DE-599)GBVOLC1984429213 (PRQ)c1651-fad7c35a1926f33d6a4a1d71bd28e4d66b33bbd67b3e947818bfe4f561e65e050 (KEY)0037705820160000063001102416onedaybayesiancloningoftype1diabetessubjectstoward DE-627 ger DE-627 rakwb eng 620 610 DE-600 XA 48665 AVZ rvk 44.09 bkl 44.40 bkl Visentin, Roberto verfasserin aut One-Day Bayesian Cloning of Type 1 Diabetes Subjects: Toward a Single-Day UVA/Padova Type 1 Diabetes Simulator 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Objective : The UVA/Padova Type 1 Diabetes (T1DM) Simulator has been shown to be representative of a T1DM population observed in a clinical trial, but has not yet been identified on T1DM data. Moreover, the current version of the simulator is "single meal" while making it "single-day centric," i.e., by describing intraday variability, would be a step forward to create more realistic in silico scenarios. Here, we propose a Bayesian method for the identification of the model from plasma glucose and insulin concentrations only, by exploiting the prior model parameter distribution. Methods : The database consists of 47 T1DM subjects, who received dinner, breakfast, and lunch (respectively, 80, 50, and 60 CHO grams) in three 23-h occasions (one open- and one closed-loop). The model is identified using the Bayesian Maximum a Posteriori technique, where the prior parameter distribution is that of the simulator. Diurnal variability of glucose absorption and insulin sensitivity is allowed. Results : The model well describes glucose traces (coefficient of determination <inline-formula><tex-math notation="LaTeX">R^{2} = 0.962 \,\pm\, 0.027 </tex-math></inline-formula>) and the posterior parameter distribution is similar to that included in the simulator. Absorption parameters at breakfast are significantly different from those at lunch and dinner, reflecting more rapid dynamics of glucose absorption. Insulin sensitivity varies in each individual but without a specific pattern. Conclusion : The incorporation of glucose absorption and insulin sensitivity diurnal variability into the simulator makes it more realistic. Significance : The proposed method, applied to the increasing number of long-term artificial pancreas studies, will allow to describe week/month variability, thus further refining the simulator. Sugar closed-loop control Sociology in silico circadian variability Bayes methods Mathematical model Artificial pancreas Plasmas compartmental modeling Data models Insulin Glucose Man, Chiara Dalla oth Cobelli, Claudio oth Enthalten in IEEE transactions on biomedical engineering New York, NY : IEEE, 1964 63(2016), 11, Seite 2416-2424 (DE-627)129358452 (DE-600)160429-6 (DE-576)01473074X 0018-9294 nnns volume:63 year:2016 number:11 pages:2416-2424 http://dx.doi.org/10.1109/TBME.2016.2535241 Volltext http://ieeexplore.ieee.org/document/7420664 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-PHA GBV_ILN_70 GBV_ILN_170 GBV_ILN_2061 GBV_ILN_2410 GBV_ILN_4219 XA 48665 44.09 AVZ 44.40 AVZ AR 63 2016 11 2416-2424
allfieldsGer	10.1109/TBME.2016.2535241 doi PQ20161201 (DE-627)OLC1984429213 (DE-599)GBVOLC1984429213 (PRQ)c1651-fad7c35a1926f33d6a4a1d71bd28e4d66b33bbd67b3e947818bfe4f561e65e050 (KEY)0037705820160000063001102416onedaybayesiancloningoftype1diabetessubjectstoward DE-627 ger DE-627 rakwb eng 620 610 DE-600 XA 48665 AVZ rvk 44.09 bkl 44.40 bkl Visentin, Roberto verfasserin aut One-Day Bayesian Cloning of Type 1 Diabetes Subjects: Toward a Single-Day UVA/Padova Type 1 Diabetes Simulator 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Objective : The UVA/Padova Type 1 Diabetes (T1DM) Simulator has been shown to be representative of a T1DM population observed in a clinical trial, but has not yet been identified on T1DM data. Moreover, the current version of the simulator is "single meal" while making it "single-day centric," i.e., by describing intraday variability, would be a step forward to create more realistic in silico scenarios. Here, we propose a Bayesian method for the identification of the model from plasma glucose and insulin concentrations only, by exploiting the prior model parameter distribution. Methods : The database consists of 47 T1DM subjects, who received dinner, breakfast, and lunch (respectively, 80, 50, and 60 CHO grams) in three 23-h occasions (one open- and one closed-loop). The model is identified using the Bayesian Maximum a Posteriori technique, where the prior parameter distribution is that of the simulator. Diurnal variability of glucose absorption and insulin sensitivity is allowed. Results : The model well describes glucose traces (coefficient of determination <inline-formula><tex-math notation="LaTeX">R^{2} = 0.962 \,\pm\, 0.027 </tex-math></inline-formula>) and the posterior parameter distribution is similar to that included in the simulator. Absorption parameters at breakfast are significantly different from those at lunch and dinner, reflecting more rapid dynamics of glucose absorption. Insulin sensitivity varies in each individual but without a specific pattern. Conclusion : The incorporation of glucose absorption and insulin sensitivity diurnal variability into the simulator makes it more realistic. Significance : The proposed method, applied to the increasing number of long-term artificial pancreas studies, will allow to describe week/month variability, thus further refining the simulator. Sugar closed-loop control Sociology in silico circadian variability Bayes methods Mathematical model Artificial pancreas Plasmas compartmental modeling Data models Insulin Glucose Man, Chiara Dalla oth Cobelli, Claudio oth Enthalten in IEEE transactions on biomedical engineering New York, NY : IEEE, 1964 63(2016), 11, Seite 2416-2424 (DE-627)129358452 (DE-600)160429-6 (DE-576)01473074X 0018-9294 nnns volume:63 year:2016 number:11 pages:2416-2424 http://dx.doi.org/10.1109/TBME.2016.2535241 Volltext http://ieeexplore.ieee.org/document/7420664 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-PHA GBV_ILN_70 GBV_ILN_170 GBV_ILN_2061 GBV_ILN_2410 GBV_ILN_4219 XA 48665 44.09 AVZ 44.40 AVZ AR 63 2016 11 2416-2424
allfieldsSound	10.1109/TBME.2016.2535241 doi PQ20161201 (DE-627)OLC1984429213 (DE-599)GBVOLC1984429213 (PRQ)c1651-fad7c35a1926f33d6a4a1d71bd28e4d66b33bbd67b3e947818bfe4f561e65e050 (KEY)0037705820160000063001102416onedaybayesiancloningoftype1diabetessubjectstoward DE-627 ger DE-627 rakwb eng 620 610 DE-600 XA 48665 AVZ rvk 44.09 bkl 44.40 bkl Visentin, Roberto verfasserin aut One-Day Bayesian Cloning of Type 1 Diabetes Subjects: Toward a Single-Day UVA/Padova Type 1 Diabetes Simulator 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Objective : The UVA/Padova Type 1 Diabetes (T1DM) Simulator has been shown to be representative of a T1DM population observed in a clinical trial, but has not yet been identified on T1DM data. Moreover, the current version of the simulator is "single meal" while making it "single-day centric," i.e., by describing intraday variability, would be a step forward to create more realistic in silico scenarios. Here, we propose a Bayesian method for the identification of the model from plasma glucose and insulin concentrations only, by exploiting the prior model parameter distribution. Methods : The database consists of 47 T1DM subjects, who received dinner, breakfast, and lunch (respectively, 80, 50, and 60 CHO grams) in three 23-h occasions (one open- and one closed-loop). The model is identified using the Bayesian Maximum a Posteriori technique, where the prior parameter distribution is that of the simulator. Diurnal variability of glucose absorption and insulin sensitivity is allowed. Results : The model well describes glucose traces (coefficient of determination <inline-formula><tex-math notation="LaTeX">R^{2} = 0.962 \,\pm\, 0.027 </tex-math></inline-formula>) and the posterior parameter distribution is similar to that included in the simulator. Absorption parameters at breakfast are significantly different from those at lunch and dinner, reflecting more rapid dynamics of glucose absorption. Insulin sensitivity varies in each individual but without a specific pattern. Conclusion : The incorporation of glucose absorption and insulin sensitivity diurnal variability into the simulator makes it more realistic. Significance : The proposed method, applied to the increasing number of long-term artificial pancreas studies, will allow to describe week/month variability, thus further refining the simulator. Sugar closed-loop control Sociology in silico circadian variability Bayes methods Mathematical model Artificial pancreas Plasmas compartmental modeling Data models Insulin Glucose Man, Chiara Dalla oth Cobelli, Claudio oth Enthalten in IEEE transactions on biomedical engineering New York, NY : IEEE, 1964 63(2016), 11, Seite 2416-2424 (DE-627)129358452 (DE-600)160429-6 (DE-576)01473074X 0018-9294 nnns volume:63 year:2016 number:11 pages:2416-2424 http://dx.doi.org/10.1109/TBME.2016.2535241 Volltext http://ieeexplore.ieee.org/document/7420664 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-PHA GBV_ILN_70 GBV_ILN_170 GBV_ILN_2061 GBV_ILN_2410 GBV_ILN_4219 XA 48665 44.09 AVZ 44.40 AVZ AR 63 2016 11 2416-2424
language	English
source	Enthalten in IEEE transactions on biomedical engineering 63(2016), 11, Seite 2416-2424 volume:63 year:2016 number:11 pages:2416-2424
sourceStr	Enthalten in IEEE transactions on biomedical engineering 63(2016), 11, Seite 2416-2424 volume:63 year:2016 number:11 pages:2416-2424
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Sugar closed-loop control Sociology in silico circadian variability Bayes methods Mathematical model Artificial pancreas Plasmas compartmental modeling Data models Insulin Glucose
dewey-raw	620
isfreeaccess_bool	false
container_title	IEEE transactions on biomedical engineering
authorswithroles_txt_mv	Visentin, Roberto @@aut@@ Man, Chiara Dalla @@oth@@ Cobelli, Claudio @@oth@@
publishDateDaySort_date	2016-01-01T00:00:00Z
hierarchy_top_id	129358452
dewey-sort	3620
id	OLC1984429213
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1984429213</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230508071808.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">161202s2016 xx \|\|\|\|\| 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/TBME.2016.2535241</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20161201</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1984429213</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1984429213</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c1651-fad7c35a1926f33d6a4a1d71bd28e4d66b33bbd67b3e947818bfe4f561e65e050</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0037705820160000063001102416onedaybayesiancloningoftype1diabetessubjectstoward</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="a">610</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">XA 48665</subfield><subfield code="q">AVZ</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.09</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.40</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Visentin, Roberto</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">One-Day Bayesian Cloning of Type 1 Diabetes Subjects: Toward a Single-Day UVA/Padova Type 1 Diabetes Simulator</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Objective : The UVA/Padova Type 1 Diabetes (T1DM) Simulator has been shown to be representative of a T1DM population observed in a clinical trial, but has not yet been identified on T1DM data. Moreover, the current version of the simulator is "single meal" while making it "single-day centric," i.e., by describing intraday variability, would be a step forward to create more realistic in silico scenarios. Here, we propose a Bayesian method for the identification of the model from plasma glucose and insulin concentrations only, by exploiting the prior model parameter distribution. Methods : The database consists of 47 T1DM subjects, who received dinner, breakfast, and lunch (respectively, 80, 50, and 60 CHO grams) in three 23-h occasions (one open- and one closed-loop). The model is identified using the Bayesian Maximum a Posteriori technique, where the prior parameter distribution is that of the simulator. Diurnal variability of glucose absorption and insulin sensitivity is allowed. Results : The model well describes glucose traces (coefficient of determination <inline-formula><tex-math notation="LaTeX">R^{2} = 0.962 \,\pm\, 0.027 </tex-math></inline-formula>) and the posterior parameter distribution is similar to that included in the simulator. Absorption parameters at breakfast are significantly different from those at lunch and dinner, reflecting more rapid dynamics of glucose absorption. Insulin sensitivity varies in each individual but without a specific pattern. Conclusion : The incorporation of glucose absorption and insulin sensitivity diurnal variability into the simulator makes it more realistic. Significance : The proposed method, applied to the increasing number of long-term artificial pancreas studies, will allow to describe week/month variability, thus further refining the simulator.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sugar</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">closed-loop control</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sociology</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">in silico</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">circadian variability</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bayes methods</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematical model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial pancreas</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Plasmas</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">compartmental modeling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Insulin</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Glucose</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Man, Chiara Dalla</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cobelli, Claudio</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">IEEE transactions on biomedical engineering</subfield><subfield code="d">New York, NY : IEEE, 1964</subfield><subfield code="g">63(2016), 11, Seite 2416-2424</subfield><subfield code="w">(DE-627)129358452</subfield><subfield code="w">(DE-600)160429-6</subfield><subfield code="w">(DE-576)01473074X</subfield><subfield code="x">0018-9294</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:63</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:11</subfield><subfield code="g">pages:2416-2424</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1109/TBME.2016.2535241</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://ieeexplore.ieee.org/document/7420664</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-DE-84</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2410</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4219</subfield></datafield><datafield tag="936" ind1="r" ind2="v"><subfield code="a">XA 48665</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.09</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.40</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">63</subfield><subfield code="j">2016</subfield><subfield code="e">11</subfield><subfield code="h">2416-2424</subfield></datafield></record></collection>
author	Visentin, Roberto
spellingShingle	Visentin, Roberto ddc 620 rvk XA 48665 bkl 44.09 bkl 44.40 misc Sugar misc closed-loop control misc Sociology misc in silico misc circadian variability misc Bayes methods misc Mathematical model misc Artificial pancreas misc Plasmas misc compartmental modeling misc Data models misc Insulin misc Glucose One-Day Bayesian Cloning of Type 1 Diabetes Subjects: Toward a Single-Day UVA/Padova Type 1 Diabetes Simulator
authorStr	Visentin, Roberto
ppnlink_with_tag_str_mv	@@773@@(DE-627)129358452
format	Article
dewey-ones	620 - Engineering & allied operations 610 - Medicine & health
delete_txt_mv	keep
author_role	aut
collection	OLC
remote_str	false
illustrated	Not Illustrated
issn	0018-9294
topic_title	620 610 DE-600 XA 48665 AVZ rvk 44.09 bkl 44.40 bkl One-Day Bayesian Cloning of Type 1 Diabetes Subjects: Toward a Single-Day UVA/Padova Type 1 Diabetes Simulator Sugar closed-loop control Sociology in silico circadian variability Bayes methods Mathematical model Artificial pancreas Plasmas compartmental modeling Data models Insulin Glucose
topic	ddc 620 rvk XA 48665 bkl 44.09 bkl 44.40 misc Sugar misc closed-loop control misc Sociology misc in silico misc circadian variability misc Bayes methods misc Mathematical model misc Artificial pancreas misc Plasmas misc compartmental modeling misc Data models misc Insulin misc Glucose
topic_unstemmed	ddc 620 rvk XA 48665 bkl 44.09 bkl 44.40 misc Sugar misc closed-loop control misc Sociology misc in silico misc circadian variability misc Bayes methods misc Mathematical model misc Artificial pancreas misc Plasmas misc compartmental modeling misc Data models misc Insulin misc Glucose
topic_browse	ddc 620 rvk XA 48665 bkl 44.09 bkl 44.40 misc Sugar misc closed-loop control misc Sociology misc in silico misc circadian variability misc Bayes methods misc Mathematical model misc Artificial pancreas misc Plasmas misc compartmental modeling misc Data models misc Insulin misc Glucose
format_facet	Aufsätze Gedruckte Aufsätze
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	nc
author2_variant	c d m cd cdm c c cc
hierarchy_parent_title	IEEE transactions on biomedical engineering
hierarchy_parent_id	129358452
dewey-tens	620 - Engineering 610 - Medicine & health
hierarchy_top_title	IEEE transactions on biomedical engineering
isfreeaccess_txt	false
familylinks_str_mv	(DE-627)129358452 (DE-600)160429-6 (DE-576)01473074X
title	One-Day Bayesian Cloning of Type 1 Diabetes Subjects: Toward a Single-Day UVA/Padova Type 1 Diabetes Simulator
ctrlnum	(DE-627)OLC1984429213 (DE-599)GBVOLC1984429213 (PRQ)c1651-fad7c35a1926f33d6a4a1d71bd28e4d66b33bbd67b3e947818bfe4f561e65e050 (KEY)0037705820160000063001102416onedaybayesiancloningoftype1diabetessubjectstoward
title_full	One-Day Bayesian Cloning of Type 1 Diabetes Subjects: Toward a Single-Day UVA/Padova Type 1 Diabetes Simulator
author_sort	Visentin, Roberto
journal	IEEE transactions on biomedical engineering
journalStr	IEEE transactions on biomedical engineering
lang_code	eng
isOA_bool	false
dewey-hundreds	600 - Technology
recordtype	marc
publishDateSort	2016
contenttype_str_mv	txt
container_start_page	2416
author_browse	Visentin, Roberto
container_volume	63
class	620 610 DE-600 XA 48665 AVZ rvk 44.09 bkl 44.40 bkl
format_se	Aufsätze
author-letter	Visentin, Roberto
doi_str_mv	10.1109/TBME.2016.2535241
dewey-full	620 610
title_sort	one-day bayesian cloning of type 1 diabetes subjects: toward a single-day uva/padova type 1 diabetes simulator
title_auth	One-Day Bayesian Cloning of Type 1 Diabetes Subjects: Toward a Single-Day UVA/Padova Type 1 Diabetes Simulator
abstract	Objective : The UVA/Padova Type 1 Diabetes (T1DM) Simulator has been shown to be representative of a T1DM population observed in a clinical trial, but has not yet been identified on T1DM data. Moreover, the current version of the simulator is "single meal" while making it "single-day centric," i.e., by describing intraday variability, would be a step forward to create more realistic in silico scenarios. Here, we propose a Bayesian method for the identification of the model from plasma glucose and insulin concentrations only, by exploiting the prior model parameter distribution. Methods : The database consists of 47 T1DM subjects, who received dinner, breakfast, and lunch (respectively, 80, 50, and 60 CHO grams) in three 23-h occasions (one open- and one closed-loop). The model is identified using the Bayesian Maximum a Posteriori technique, where the prior parameter distribution is that of the simulator. Diurnal variability of glucose absorption and insulin sensitivity is allowed. Results : The model well describes glucose traces (coefficient of determination <inline-formula><tex-math notation="LaTeX">R^{2} = 0.962 \,\pm\, 0.027 </tex-math></inline-formula>) and the posterior parameter distribution is similar to that included in the simulator. Absorption parameters at breakfast are significantly different from those at lunch and dinner, reflecting more rapid dynamics of glucose absorption. Insulin sensitivity varies in each individual but without a specific pattern. Conclusion : The incorporation of glucose absorption and insulin sensitivity diurnal variability into the simulator makes it more realistic. Significance : The proposed method, applied to the increasing number of long-term artificial pancreas studies, will allow to describe week/month variability, thus further refining the simulator.
abstractGer	Objective : The UVA/Padova Type 1 Diabetes (T1DM) Simulator has been shown to be representative of a T1DM population observed in a clinical trial, but has not yet been identified on T1DM data. Moreover, the current version of the simulator is "single meal" while making it "single-day centric," i.e., by describing intraday variability, would be a step forward to create more realistic in silico scenarios. Here, we propose a Bayesian method for the identification of the model from plasma glucose and insulin concentrations only, by exploiting the prior model parameter distribution. Methods : The database consists of 47 T1DM subjects, who received dinner, breakfast, and lunch (respectively, 80, 50, and 60 CHO grams) in three 23-h occasions (one open- and one closed-loop). The model is identified using the Bayesian Maximum a Posteriori technique, where the prior parameter distribution is that of the simulator. Diurnal variability of glucose absorption and insulin sensitivity is allowed. Results : The model well describes glucose traces (coefficient of determination <inline-formula><tex-math notation="LaTeX">R^{2} = 0.962 \,\pm\, 0.027 </tex-math></inline-formula>) and the posterior parameter distribution is similar to that included in the simulator. Absorption parameters at breakfast are significantly different from those at lunch and dinner, reflecting more rapid dynamics of glucose absorption. Insulin sensitivity varies in each individual but without a specific pattern. Conclusion : The incorporation of glucose absorption and insulin sensitivity diurnal variability into the simulator makes it more realistic. Significance : The proposed method, applied to the increasing number of long-term artificial pancreas studies, will allow to describe week/month variability, thus further refining the simulator.
abstract_unstemmed	Objective : The UVA/Padova Type 1 Diabetes (T1DM) Simulator has been shown to be representative of a T1DM population observed in a clinical trial, but has not yet been identified on T1DM data. Moreover, the current version of the simulator is "single meal" while making it "single-day centric," i.e., by describing intraday variability, would be a step forward to create more realistic in silico scenarios. Here, we propose a Bayesian method for the identification of the model from plasma glucose and insulin concentrations only, by exploiting the prior model parameter distribution. Methods : The database consists of 47 T1DM subjects, who received dinner, breakfast, and lunch (respectively, 80, 50, and 60 CHO grams) in three 23-h occasions (one open- and one closed-loop). The model is identified using the Bayesian Maximum a Posteriori technique, where the prior parameter distribution is that of the simulator. Diurnal variability of glucose absorption and insulin sensitivity is allowed. Results : The model well describes glucose traces (coefficient of determination <inline-formula><tex-math notation="LaTeX">R^{2} = 0.962 \,\pm\, 0.027 </tex-math></inline-formula>) and the posterior parameter distribution is similar to that included in the simulator. Absorption parameters at breakfast are significantly different from those at lunch and dinner, reflecting more rapid dynamics of glucose absorption. Insulin sensitivity varies in each individual but without a specific pattern. Conclusion : The incorporation of glucose absorption and insulin sensitivity diurnal variability into the simulator makes it more realistic. Significance : The proposed method, applied to the increasing number of long-term artificial pancreas studies, will allow to describe week/month variability, thus further refining the simulator.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-PHA GBV_ILN_70 GBV_ILN_170 GBV_ILN_2061 GBV_ILN_2410 GBV_ILN_4219
container_issue	11
title_short	One-Day Bayesian Cloning of Type 1 Diabetes Subjects: Toward a Single-Day UVA/Padova Type 1 Diabetes Simulator
url	http://dx.doi.org/10.1109/TBME.2016.2535241 http://ieeexplore.ieee.org/document/7420664
remote_bool	false
author2	Man, Chiara Dalla Cobelli, Claudio
author2Str	Man, Chiara Dalla Cobelli, Claudio
ppnlink	129358452
mediatype_str_mv	n
isOA_txt	false
hochschulschrift_bool	false
author2_role	oth oth
doi_str	10.1109/TBME.2016.2535241
up_date	2024-07-04T00:22:46.443Z
_version_	1803605839911583744
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1984429213</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230508071808.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">161202s2016 xx \|\|\|\|\| 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1109/TBME.2016.2535241</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20161201</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1984429213</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1984429213</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)c1651-fad7c35a1926f33d6a4a1d71bd28e4d66b33bbd67b3e947818bfe4f561e65e050</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0037705820160000063001102416onedaybayesiancloningoftype1diabetessubjectstoward</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="a">610</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">XA 48665</subfield><subfield code="q">AVZ</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.09</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.40</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Visentin, Roberto</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">One-Day Bayesian Cloning of Type 1 Diabetes Subjects: Toward a Single-Day UVA/Padova Type 1 Diabetes Simulator</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Objective : The UVA/Padova Type 1 Diabetes (T1DM) Simulator has been shown to be representative of a T1DM population observed in a clinical trial, but has not yet been identified on T1DM data. Moreover, the current version of the simulator is "single meal" while making it "single-day centric," i.e., by describing intraday variability, would be a step forward to create more realistic in silico scenarios. Here, we propose a Bayesian method for the identification of the model from plasma glucose and insulin concentrations only, by exploiting the prior model parameter distribution. Methods : The database consists of 47 T1DM subjects, who received dinner, breakfast, and lunch (respectively, 80, 50, and 60 CHO grams) in three 23-h occasions (one open- and one closed-loop). The model is identified using the Bayesian Maximum a Posteriori technique, where the prior parameter distribution is that of the simulator. Diurnal variability of glucose absorption and insulin sensitivity is allowed. Results : The model well describes glucose traces (coefficient of determination <inline-formula><tex-math notation="LaTeX">R^{2} = 0.962 \,\pm\, 0.027 </tex-math></inline-formula>) and the posterior parameter distribution is similar to that included in the simulator. Absorption parameters at breakfast are significantly different from those at lunch and dinner, reflecting more rapid dynamics of glucose absorption. Insulin sensitivity varies in each individual but without a specific pattern. Conclusion : The incorporation of glucose absorption and insulin sensitivity diurnal variability into the simulator makes it more realistic. Significance : The proposed method, applied to the increasing number of long-term artificial pancreas studies, will allow to describe week/month variability, thus further refining the simulator.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sugar</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">closed-loop control</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sociology</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">in silico</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">circadian variability</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bayes methods</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematical model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial pancreas</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Plasmas</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">compartmental modeling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Insulin</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Glucose</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Man, Chiara Dalla</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cobelli, Claudio</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">IEEE transactions on biomedical engineering</subfield><subfield code="d">New York, NY : IEEE, 1964</subfield><subfield code="g">63(2016), 11, Seite 2416-2424</subfield><subfield code="w">(DE-627)129358452</subfield><subfield code="w">(DE-600)160429-6</subfield><subfield code="w">(DE-576)01473074X</subfield><subfield code="x">0018-9294</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:63</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:11</subfield><subfield code="g">pages:2416-2424</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1109/TBME.2016.2535241</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://ieeexplore.ieee.org/document/7420664</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-DE-84</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2410</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4219</subfield></datafield><datafield tag="936" ind1="r" ind2="v"><subfield code="a">XA 48665</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.09</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.40</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">63</subfield><subfield code="j">2016</subfield><subfield code="e">11</subfield><subfield code="h">2416-2424</subfield></datafield></record></collection>
score	7.3992996

Nicht das Richtige dabei?

Schreiben Sie uns!

One-Day Bayesian Cloning of Type 1 Diabetes Subjects: Toward a Single-Day UVA/Padova Type 1 Diabetes Simulator

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?