東華大學圖書館 |

AI for disease surveillance and pandemic intelligence = intelligent disease detection in action /

Record Type:	Electronic resources : Monograph/item
Title/Author:	AI for disease surveillance and pandemic intelligence/ edited by Arash Shaban-Nejad, Martin Michalowski, Simone Bianco.
Reminder of title:	intelligent disease detection in action /
other author:	Shaban-Nejad, Arash.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xix, 331 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence - Medical applications. -
Online resource:	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 intelligenceintelligent disease detection in action /[electronic resource] :edited by Arash Shaban-Nejad, Martin Michalowski, Simone Bianco. - Cham :Springer International Publishing :2022. - xix, 331 p. :ill., digital ;24 cm. - Studies in computational intelligence,v. 10131860-9503 ;. - Studies in computational intelligence ;v. 1013..

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:

900591
Artificial intelligence
--Medical applications.

LC Class. No.: R859.7.A78 / A5 2022

Dewey Class. No.: 610.28563

AI for disease surveillance and pandemic intelligence = intelligent disease detection in action /
LDR:04114nmm a2200337 a 4500 001 2298986
003 DE-He213
005 20220308184452.0
006 m d
007 cr nn 008maaau
008 230324s2022 sz s 0 eng d
020 $a 9783030930806 $q (electronic bk.)
020 $a 9783030930790 $q (paper)
024 7 $a 10.1007/978-3-030-93080-6 $2 doi
035 $a 978-3-030-93080-6
040 $a GP $c GP
041 0 $a eng
050 4 $a R859.7.A78 $b A5 2022
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 610.28563 $2 23
090 $a R859.7.A78 $b A288 2022
245 0 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.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xix, 331 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-9503 ; $v 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 0 $a Artificial intelligence $x Medical applications. $3 900591
650 0 $a Machine learning. $3 533906
650 0 $a Diseases $x Data processing. $3 3596059
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Biomedical Engineering and Bioengineering. $3 3381533
650 2 4 $a Data Engineering. $3 3409361
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Shaban-Nejad, Arash. $3 3258638
700 1 $a Michalowski, Martin. $3 3443413
700 1 $a Bianco, Simone. $3 3226651
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Studies in computational intelligence ; $v v. 1013. $3 3596058
856 4 0 $u https://doi.org/10.1007/978-3-030-93080-6
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)