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
AI for Disease Surveillance and Pandemic Intelligence = Intelligent Disease Detection in Action /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
AI for Disease Surveillance and Pandemic Intelligence/ edited by Arash Shaban-Nejad, Martin Michalowski, Simone Bianco.
其他題名:
Intelligent Disease Detection in Action /
其他作者:
Bianco, Simone.
面頁冊數:
XIX, 331 p. 87 illus., 63 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Artificial Intelligence. -
電子資源:
https://doi.org/10.1007/978-3-030-93080-6
ISBN:
9783030930806
AI for Disease Surveillance and Pandemic Intelligence = Intelligent Disease Detection in Action /
AI for Disease Surveillance and Pandemic Intelligence
Intelligent Disease Detection in Action /[electronic resource] :edited by Arash Shaban-Nejad, Martin Michalowski, Simone Bianco. - 1st ed. 2022. - XIX, 331 p. 87 illus., 63 illus. in color.online resource. - Studies in Computational Intelligence,10131860-9503 ;. - Studies in Computational Intelligence,564.
Digital Technologies for Clinical, Public and Global Health Surveillance -- Imputing Fine-grain Patterns of Mental Health with Statistical Modelling of Online Data -- Lexical and Acoustic Correlates of Clinical Speech Disturbance in Schizo-phrenia.-A Prognostic Tool to Identify Youth at Risk of at Least Weekly Cannabis Use -- Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Pro-grammed Deep Kernels.-Self-Disclosure in Opioid Use Recovery Forums -- Identifying Prepubertal Children with Risk for Suicide Using Deep Neural Network Trained on Multimodal Brain Imaging -- Improving Adverse Drug Event Extraction with SpanBERT on Different Text Typologies -- Machine Learning Identification of Self-Reported COVID-19 Symptoms from Tweets in Canada -- RRISK: Analyzing COVID-19 Risk in Food Establishments -- AWS CORD-19 Search: A Neural Search Engine for COVID-19 Literature -- Inferring COVID-19 Biological Pathways from Clinical Phenotypes via Topological Analysis -- The EpiBench Platform to Propel AI/ML-based Epidemic Forecasting: A Prototype Demonstration Reaching Human Expert-level Performance -- Interpretable Classification of Human Exercise Videos through Pose Esti-mation and Multivariate Time Series Analysis -- Interpreting Deep Neural Networks for Medical Imaging using Concept Graphs -- Do Deep Neural Networks Forget Facial Action Units? - Exploring the Ef-fects of Transfer Learning in Health Related Facial Expression Recognition -- Utilizing Predictive Analysis to Aid Emergency Medical Services -- Measuring Physiological Markers of Stress During Conversational Agent Interactions -- EvSys: A Relational Dynamic System for Sparse Irregular Clinical Events -- Predicting Patient Outcomes with Graph Representation Learning -- Patient-Specific Seizure Prediction Using Single Seizure Electroenceph-alography Recording -- Evaluation Metrics for Deep Learning Imputation Models -- Logistic Regression Is Also A Black Box. Machine Learning Can Help.
This book aims to highlight the latest achievements in the use of artificial intelligence for digital disease surveillance, pandemic intelligence, as well as public and clinical health surveillance. The edited book contains selected papers presented at the 2021 Health Intelligence workshop, co-located with the Association for the Advancement of Artificial Intelligence (AAAI) annual conference, and presents an overview of the issues, challenges, and potentials in the field, along with new research results. While disease surveillance has always been a crucial process, the recent global health crisis caused by COVID-19 has once again highlighted our dependence on intelligent surveillance infrastructures that provide support for making sound and timely decisions. This book provides information for researchers, students, industry professionals, and public health agencies interested in the applications of AI in population health and personalized medicine.
ISBN: 9783030930806
Standard No.: 10.1007/978-3-030-93080-6doiSubjects--Topical Terms:
646849
Artificial Intelligence.
LC Class. No.: Q342
Dewey Class. No.: 006.3
AI for Disease Surveillance and Pandemic Intelligence = Intelligent Disease Detection in Action /
LDR
:04486nam a22004095i 4500
001
1089828
003
DE-He213
005
20220308184452.0
007
cr nn 008mamaa
008
221228s2022 sz | s |||| 0|eng d
020
$a
9783030930806
$9
978-3-030-93080-6
024
7
$a
10.1007/978-3-030-93080-6
$2
doi
035
$a
978-3-030-93080-6
050
4
$a
Q342
072
7
$a
UYQ
$2
bicssc
072
7
$a
TEC009000
$2
bisacsh
072
7
$a
UYQ
$2
thema
082
0 4
$a
006.3
$2
23
245
1 0
$a
AI for Disease Surveillance and Pandemic Intelligence
$h
[electronic resource] :
$b
Intelligent Disease Detection in Action /
$c
edited by Arash Shaban-Nejad, Martin Michalowski, Simone Bianco.
250
$a
1st ed. 2022.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2022.
300
$a
XIX, 331 p. 87 illus., 63 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
490
1
$a
Studies in Computational Intelligence,
$x
1860-9503 ;
$v
1013
505
0
$a
Digital Technologies for Clinical, Public and Global Health Surveillance -- Imputing Fine-grain Patterns of Mental Health with Statistical Modelling of Online Data -- Lexical and Acoustic Correlates of Clinical Speech Disturbance in Schizo-phrenia.-A Prognostic Tool to Identify Youth at Risk of at Least Weekly Cannabis Use -- Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Pro-grammed Deep Kernels.-Self-Disclosure in Opioid Use Recovery Forums -- Identifying Prepubertal Children with Risk for Suicide Using Deep Neural Network Trained on Multimodal Brain Imaging -- Improving Adverse Drug Event Extraction with SpanBERT on Different Text Typologies -- Machine Learning Identification of Self-Reported COVID-19 Symptoms from Tweets in Canada -- RRISK: Analyzing COVID-19 Risk in Food Establishments -- AWS CORD-19 Search: A Neural Search Engine for COVID-19 Literature -- Inferring COVID-19 Biological Pathways from Clinical Phenotypes via Topological Analysis -- The EpiBench Platform to Propel AI/ML-based Epidemic Forecasting: A Prototype Demonstration Reaching Human Expert-level Performance -- Interpretable Classification of Human Exercise Videos through Pose Esti-mation and Multivariate Time Series Analysis -- Interpreting Deep Neural Networks for Medical Imaging using Concept Graphs -- Do Deep Neural Networks Forget Facial Action Units? - Exploring the Ef-fects of Transfer Learning in Health Related Facial Expression Recognition -- Utilizing Predictive Analysis to Aid Emergency Medical Services -- Measuring Physiological Markers of Stress During Conversational Agent Interactions -- EvSys: A Relational Dynamic System for Sparse Irregular Clinical Events -- Predicting Patient Outcomes with Graph Representation Learning -- Patient-Specific Seizure Prediction Using Single Seizure Electroenceph-alography Recording -- Evaluation Metrics for Deep Learning Imputation Models -- Logistic Regression Is Also A Black Box. Machine Learning Can Help.
520
$a
This book aims to highlight the latest achievements in the use of artificial intelligence for digital disease surveillance, pandemic intelligence, as well as public and clinical health surveillance. The edited book contains selected papers presented at the 2021 Health Intelligence workshop, co-located with the Association for the Advancement of Artificial Intelligence (AAAI) annual conference, and presents an overview of the issues, challenges, and potentials in the field, along with new research results. While disease surveillance has always been a crucial process, the recent global health crisis caused by COVID-19 has once again highlighted our dependence on intelligent surveillance infrastructures that provide support for making sound and timely decisions. This book provides information for researchers, students, industry professionals, and public health agencies interested in the applications of AI in population health and personalized medicine.
650
2 4
$a
Artificial Intelligence.
$3
646849
650
2 4
$a
Data Engineering.
$3
1226308
650
2 4
$a
Biomedical Engineering and Bioengineering.
$3
1211019
650
1 4
$a
Computational Intelligence.
$3
768837
650
0
$a
Artificial intelligence.
$3
559380
650
0
$a
Engineering—Data processing.
$3
1297966
650
0
$a
Biomedical engineering.
$3
588770
650
0
$a
Computational intelligence.
$3
568984
700
1
$a
Bianco, Simone.
$4
edt
$4
http://id.loc.gov/vocabulary/relators/edt
$3
1139687
700
1
$a
Michalowski, Martin.
$e
editor.
$1
https://orcid.org/0000-0003-2060-5878
$4
edt
$4
http://id.loc.gov/vocabulary/relators/edt
$3
1313590
700
1
$a
Shaban-Nejad, Arash.
$4
edt
$4
http://id.loc.gov/vocabulary/relators/edt
$3
1200881
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783030930790
776
0 8
$i
Printed edition:
$z
9783030930813
776
0 8
$i
Printed edition:
$z
9783030930820
830
0
$a
Studies in Computational Intelligence,
$x
1860-949X ;
$v
564
$3
1253640
856
4 0
$u
https://doi.org/10.1007/978-3-030-93080-6
912
$a
ZDB-2-INR
912
$a
ZDB-2-SXIT
950
$a
Intelligent Technologies and Robotics (SpringerNature-42732)
950
$a
Intelligent Technologies and Robotics (R0) (SpringerNature-43728)
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