語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Personalized predictive modeling in ...
~
Fotiadis, Dimitrios Ioannou,
Personalized predictive modeling in Type 1 diabetes
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Personalized predictive modeling in Type 1 diabetes/ Eleni I. Georga, Dimitrios I. Fotiadis, Stelios K. Tigas.
作者:
Georga, Eleni I.,
其他作者:
Tigas, Stelios K.,
出版者:
London :Academic Press, an imprint of Elsevier, : 2018.,
面頁冊數:
1 online resource.
標題:
MEDICAL - Internal Medicine. -
電子資源:
https://www.sciencedirect.com/science/book/9780128048313
ISBN:
9780128051467 (electronic bk.)
Personalized predictive modeling in Type 1 diabetes
Georga, Eleni I.,
Personalized predictive modeling in Type 1 diabetes
[electronic resource] /Eleni I. Georga, Dimitrios I. Fotiadis, Stelios K. Tigas. - London :Academic Press, an imprint of Elsevier,2018. - 1 online resource.
Includes bibliographical references.
Personalized Predictive Modeling in Diabetes features state-of-the-art methodologies and algorithmic approaches which have been applied to predictive modeling of glucose concentration, ranging from simple autoregressive models of the CGM time series to multivariate nonlinear regression techniques of machine learning. Developments in the field have been analyzed with respect to: (i) feature set (univariate or multivariate), (ii) regression technique (linear or non-linear), (iii) learning mechanism (batch or sequential), (iv) development and testing procedure and (v) scaling properties. In addition, simulation models of meal-derived glucose absorption and insulin dynamics and kinetics are covered, as an integral part of glucose predictive models. This book will help engineers and clinicians to: select a regression technique which can capture both linear and non-linear dynamics in glucose metabolism in diabetes, and which exhibits good generalization performance under stationary and non-stationary conditions; ensure the scalability of the optimization algorithm (learning mechanism) with respect to the size of the dataset, provided that multiple days of patient monitoring are needed to obtain a reliable predictive model; select a features set which efficiently represents both spatial and temporal dependencies between the input variables and the glucose concentration; select simulation models of subcutaneous insulin absorption and meal absorption; identify an appropriate validation procedure, and identify realistic performance measures.
ISBN: 9780128051467 (electronic bk.)Subjects--Topical Terms:
862781
MEDICAL
--Internal Medicine.Index Terms--Genre/Form:
554714
Electronic books.
LC Class. No.: RC660
Dewey Class. No.: 616.462
National Library of Medicine Call No.: 2018 C-341
Personalized predictive modeling in Type 1 diabetes
LDR
:02592cam a2200277 a 4500
001
1042829
006
m o d
007
cr cnu|||unuuu
008
211216s2018 enka gob 000 0 eng d
020
$a
9780128051467 (electronic bk.)
020
$a
0128051469 (electronic bk.)
020
$a
9780128048313
020
$a
012804831X
035
$a
(OCoLC)1013541224
035
$a
on1013541224
040
$a
N$T
$b
eng
$c
N$T
$d
EBLCP
$d
N$T
$d
OPELS
$d
IDEBK
$d
UPM
$d
STF
$d
MERER
$d
OCLCQ
$d
IAY
$d
D6H
$d
YDX
$d
UAB
$d
U3W
$d
OCLCF
$d
OCLCQ
$d
COD
$d
ESU
$d
WYU
$d
OCLCA
$d
LVT
$d
OCLCA
$d
OCLCQ
$d
OCLCO
$d
S2H
$d
OCLCO
$d
VT2
$d
OCLCA
$d
OCLCQ
$d
OCLCO
041
0
$a
eng
050
4
$a
RC660
060
4
$a
2018 C-341
060
4
$a
WK 810
082
0 4
$a
616.462
$2
23
100
1
$a
Georga, Eleni I.,
$e
author.
$3
1343405
245
1 0
$a
Personalized predictive modeling in Type 1 diabetes
$h
[electronic resource] /
$c
Eleni I. Georga, Dimitrios I. Fotiadis, Stelios K. Tigas.
260
$a
London :
$b
Academic Press, an imprint of Elsevier,
$c
2018.
300
$a
1 online resource.
504
$a
Includes bibliographical references.
520
$a
Personalized Predictive Modeling in Diabetes features state-of-the-art methodologies and algorithmic approaches which have been applied to predictive modeling of glucose concentration, ranging from simple autoregressive models of the CGM time series to multivariate nonlinear regression techniques of machine learning. Developments in the field have been analyzed with respect to: (i) feature set (univariate or multivariate), (ii) regression technique (linear or non-linear), (iii) learning mechanism (batch or sequential), (iv) development and testing procedure and (v) scaling properties. In addition, simulation models of meal-derived glucose absorption and insulin dynamics and kinetics are covered, as an integral part of glucose predictive models. This book will help engineers and clinicians to: select a regression technique which can capture both linear and non-linear dynamics in glucose metabolism in diabetes, and which exhibits good generalization performance under stationary and non-stationary conditions; ensure the scalability of the optimization algorithm (learning mechanism) with respect to the size of the dataset, provided that multiple days of patient monitoring are needed to obtain a reliable predictive model; select a features set which efficiently represents both spatial and temporal dependencies between the input variables and the glucose concentration; select simulation models of subcutaneous insulin absorption and meal absorption; identify an appropriate validation procedure, and identify realistic performance measures.
650
7
$a
MEDICAL
$x
Internal Medicine.
$2
bisacsh
$3
862781
650
7
$a
MEDICAL
$x
Evidence-Based Medicine.
$2
bisacsh
$3
862780
650
7
$a
MEDICAL
$x
Diseases.
$2
bisacsh
$3
862500
650
7
$a
MEDICAL
$x
Clinical Medicine.
$2
bisacsh
$3
812408
650
7
$a
HEALTH & FITNESS
$x
Diseases
$x
General.
$2
bisacsh
$3
862499
650
2 2
$a
Models, Theoretical.
$3
528501
650
2 2
$a
Blood Glucose Self-Monitoring.
$3
1343409
650
1 2
$a
Diabetes Mellitus, Type 1.
$3
778229
650
0
$a
Blood sugar monitoring.
$3
1208774
650
0
$a
Glucose
$x
Mathematical models.
$3
1343408
650
0
$a
Diabetes.
$3
582755
655
4
$a
Electronic books.
$2
local
$3
554714
700
1
$a
Tigas, Stelios K.,
$e
author.
$3
1343407
700
1
$a
Fotiadis, Dimitrios Ioannou,
$e
author.
$3
1343406
856
4 0
$u
https://www.sciencedirect.com/science/book/9780128048313
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入