Kooperativer Bibliotheksverbund

UID:

Format: 1 online resource (xxvi, 521 pages)

ISBN: 0-12-823338-9

Note: Machine generated contents note: Introduction to artificial intelligence -- , Basic Concepts of Artificial Intelligence -- , Definitions -- , Types of Artificial Intelligence -- , Artificial Intelligence and Data Science -- , The Human -- Machine Intelligence Continuum -- , The Analytics Continuum -- , Artificial Intelligence and the Neurosciences -- , The Doctor's Brain and Machine Intelligence -- , References -- , History of Artificial Intelligence -- , Key People and Events -- , Alan Turing and the Turing Machine -- , The Dartmouth Conference -- , Rosenblatt's Perceptron -- , Key Epochs and Movements -- , Good Old-fashioned Artificial Intelligence -- , Computational Intelligence -- , The Artificial Intelligence Winters -- , References -- , History of Artificial Intelligence in Medicine -- , Rule-based Expert Systems -- , Other Artificial Intelligence Methodologies -- , Failure of Adoption -- , Ten Common Misconceptions of Artificial Intelligence in Medicine -- , References -- , Key Concepts -- , Data science and artificial intelli' gence in the current era -- , Health-care Data and Databases -- , Health-care Data -- , Big Data -- , Health-care Data Conundrum -- , Health-care Data Management -- , Data Processing and Storage -- , Electronic Medical Record Adoption and Interoperability -- , Health-care Databases -- , Database-management Systems -- , Relational Database -- , Object-oriented Database -- , Graph Database -- , The Data-to-intelligence Continuum and Artificial Intelligence -- , References -- , Machine and Deep Learning -- , Introduction to Machine Learning -- , Data Mining and Knowledge Discovery -- , History and Current State of Machine Learning -- , Machine Learning versus Conventional Programing -- , Machine Learning Workflow -- , Data Science in Biomedicine -- , Programing Languages in Biomedical Data Science -- , Classical Machine Learning -- , Supervised Learning -- , Unsupervised Learning -- , Ensemble Learning -- , Reinforcement Learning -- , Neural Networks and Deep Learning -- , Perceptron and Multilayer Perceptrons -- , Deep Learning -- , Autoencoder Neural Network -- , Assessment of Model Performance -- , Assessment Methods -- , Evaluation of Regression Models -- , Evaluation of Classification Models -- , Fundamental Issues in Machine and Deep Learning -- , Interpretability and Explainability -- , Bias and Variance Trade-off -- , Fitting -- , Curse of Dimensionality -- , Correlation versus Causation -- , Machine versus Deep Learning -- , References -- , Other Key Concepts in Artificial Intelligence -- , Cognitive Computing -- , Natural Language Processing -- , Robotics -- , Autonomous Systems -- , Robotic Process Automation -- , Other Key Technologies Related to Artificial Intelligence -- , Augmented and virtual reality -- , Blockchain -- , Cloud -- , Cybersecurity -- , Internet of Things -- , Key Issues Related to Artificial Intelligence -- , Bias -- , Ethics -- , Safety -- , Legal -- , Key Concepts -- , Ten Questions to Assess your Data Science and Artificial Intelligence Knowledge -- , Ten Steps to Become More Knowledgeable in Artificial Intelligence in Medicine -- , References -- , The current era of artificial intelligence in medicine -- , Clinician Cognition and Artificial Intelligence in Medicine -- , The Rationale for Intelligence-based Medicine -- , Adoption of Artificial Intelligence in Medicine: The Challenges Ahead -- , Clinician Cognition and Artificial Intelligence in Medicine -- , Current Artificial Intelligence in Medicine Applications -- , References -- , Artificial Intelligence in Subspecialties -- , The Present State of Artificial Intelligence in Subspecialties -- , The subspecialties and artificial intelligence strategy and applications -- , Artificial Intelligence Applications for all Subspecialties -- , Anesthesiology -- , Cardiology (Adult and Pediatric) and Cardiac Surgery -- , Critical Care Medicine -- , Dermatology -- , Emergency Medicine -- , Endocrinology -- , Gastroenterology -- , Global and Public Health/Epidemiology -- , Hematology -- , Infectious Disease -- , Internal and Family Medicine/Primary Care -- , Nephrology -- , Neurosciences (Neurology/Neurosurgery and Psychiatry/Psychology) -- , Obstetrics/Gynecology -- , Oncology -- , Ophthalmology -- , Pathology -- , Pediatrics -- , Pulmonology -- , Radiology -- , Rheumatology -- , Surgery -- , Dentistry -- , Digital Health -- , Genomic Medicine and Precision/Personalized Medicine -- , Physical Medicine and Rehabilitation -- , Regenerative Medicine -- , Veterinary Medicine -- , Medical Education and Training -- , Nursing -- , Healthcare Administration -- , References -- , Implementation of Artificial Intelligence in Medicine -- , Key Concepts -- , Assessment of Artificial Intelligence Readiness in Health-care Organizations -- , Ten Elements for Successful Implementation of Artificial Intelligence in Medicine -- , Ten Obstacles to Overcome for Implementation of Artificial Intelligence in Medicine -- , References -- , The future of artificial intelligence and application in medicine -- , Key Concepts of the Future of Artificial Intelligence -- , 5G -- , Augmented and Virtual Reality -- , Blockchain and Cybersecurity -- , Brain -- Computer Interface -- , Capsule Network -- , Cloud Artificial Intelligence -- , Edge Computing -- , Embedded Artificial Intelligence (or Internet of Everything) -- , Fuzzy Cognitive Maps -- , Generative Query Network -- , Hypergraph Database -- , Low-shot Learning -- , Neuromorphic Computing -- , Quantum Computing -- , Recursive Cortical Network -- , Spiking Neural Network (SNN) -- , Swarm Intelligence -- , Temporal Convolutional Nets -- , Transfer Learning -- , Data and Databases -- , References -- , The Future of Artificial Intelligence in Medicine -- , Key Concepts -- , References.

Additional Edition: ISBN 0-12-823337-0

Language: English

UID:

Format: 1 online resource (542 pages)

Edition: 1st ed.

ISBN: 0-323-90629-X

Series Statement: Intelligence-Based Medicine: Subspecialty Series

Content: Intelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine provides an especially timely multidisciplinary and comprehensive survey of artificial intelligence concepts and methodologies. It includes real-life applications in adult and pediatric cardiovascular medicine, spanning the life span from fetus to adult. Led by a senior cardiologist–data scientist and supported by renowned data scientists and cardiac clinicians with an ardent passion for artificial intelligence in cardiovascular medicine, the book provides a clinical interface between the medical and data science domains that is symmetric and realistic.

Note: Front Cover -- Intelligence-Based Cardiology and Cardiac Surgery -- Intelligence-Based Cardiology and Cardiac Surgery -- Copyright -- Dedication -- Contents -- Contributors -- About the editors -- Foreword by Eric Topol -- Foreword by Ami Bhatt -- Preface -- Acknowledgments -- I - Basic concepts of data science and artificial intelligence -- 1 - Introduction to artificial intelligence for cardiovascular clinicians -- Basic concepts of artificial intelligence -- Definitions and concepts -- Artificial intelligence and the neurosciences -- History of artificial intelligence -- Key people and events -- Key epochs and movements -- History of artificial intelligence in medicine -- AI methodologies -- AI adoption -- Healthcare data and databases -- Cardiology data -- Healthcare data management -- Healthcare databases -- The data-to-intelligence continuum and AI -- Machine and deep learning -- Introduction to machine learning -- Classical machine learning and other types of learning -- Supervised learning -- Classification -- Regression -- Unsupervised learning -- Clustering -- Generalization (or dimension reduction) -- Semisupervised and self-supervised learning -- Ensemble learning -- Reinforcement learning -- Neural networks and deep learning -- Perceptron and multilayer perceptrons (MLP) -- Autoencoder neural network -- Generative adversarial network (GAN) -- Convolutional neural network (CNN) -- Recurrent neural network (RNN) -- Transfer learning -- Transformers -- Assessment of model performance -- Assessment methods -- Evaluation of regression models -- Evaluation of classification models -- Fundamental issues in machine and deep learning -- Model and data drift -- Model parameters and hyperparameters -- Imbalanced data set -- Interpretability and explainability -- Bias and variance trade-off -- Fitting of a model -- Curse of dimensionality. , Correlation versus causation -- Machine versus deep learning -- Other key concepts and technologies in artificial intelligence -- Key concepts -- Bias and equity -- Complicated versus complex -- Healthcare cybersecurity -- Health economics -- Ethical considerations -- Education in clinicians -- Legal issues -- Overdiagnosis -- Regulatory issues -- Safety -- Key technologies -- Autonomous systems -- Blockchain -- Cloud technology -- Cognitive computing -- Digital twins -- Edge computing and embedded AI -- Extended reality -- Federated and swarm intelligence -- Foundation model -- Internet of things and everything (IoT and IoE) -- Knowledge graphs -- Low-shot learning -- Metaverse -- Monte Carlo simulation (MCS) -- Natural language processing (NLP) -- Robotics -- Robotic process automation (RPA) -- Human cognition and artificial intelligence in cardiology -- System 1 and system 2 thinking -- Uncertainty in biomedicine -- Clinician cognitive biases and heuristics -- Logical reasoning -- Evidence-based medicine -- Clinician perception/cognition -- Clinician-AI synergy: cognition-based AI -- Current status of AI in medicine and relevance to cardiovascular medicine -- The "why" for intelligence-based cardiology: sanctuary for the perfect storm -- Current areas of AI in medicine with relevance to cardiovascular medicine -- Medical imaging -- Extended reality -- Decision support -- Biomedical diagnostics -- Precision medicine -- Drug discovery -- Digital health -- Wearable technology -- Robotic technology -- Virtual assistance -- Current and future state of AI in cardiology -- Adoption of AI in cardiovascular medicine: the challenges ahead -- References -- II - Artificial intelligence in cardiovascular medicine -- A - Basic concepts of artificial intelligence in cardiology and cardiac surgery. , 2 - Application of artificial intelligence in cardiovascular medicine and cardiac surgery -- Introduction -- Preview of the future -- Why is cardiology special for AI adoption? -- Current state of the art -- Data registries -- Augmented diagnostics, clinical decision support, risk prediction, and treatment -- Precision cardiology -- Potential opportunities for AI implementation in cardiology -- Future directions -- Challenges in implementation of AI in cardiology -- Strategies to overcome challenges in implementation of AI in cardiology -- Major takeaways -- Intelligence-based cardiac care -- References -- Further reading -- 3 - Data and databases in cardiovascular medicine and surgery -- Introduction -- Current state of the art -- Future directions -- Major takeaways -- References -- 4 - Data and databases for pediatric and adult congenital cardiac care -- Introduction -- Current state of the art -- Nomenclature -- Database -- Risk adjustment -- Verification of the completeness and accuracy of the data -- Collaboration across medical and surgical subspecialties -- Linking of databases and registries -- Longitudinal follow-up -- Assessment and improvement of quality -- Future directions -- Improving the discrimination and calibration of risk models with machine learning and other novel approaches -- Minimizing the burden to enter data and thereby minimizing the associated costs -- Documenting longitudinal outcomes consistently and effortlessly -- Measuring the value of healthcare -- Utilizing the massive amount of available data in our cardiac intensive care units with streaming analytics -- Decreasing the costs associated with research and the generation of new knowledge by conducting randomized trials within re ... -- Linking genotypic data to phenotypic data within registries. , Utilizing pediatric and congenital cardiac databases to facilitate personalized precision medicine -- Major takeaways -- References -- 5 - Cognitive biases and heuristics in human cognition -- Introduction -- How do doctors make decisions? -- Our decision making environment -- Impact of culture on decision making -- Asking the right question -- Contrarians who seeing the unseen -- Utility theory and cognitive bias (Kahneman) -- The Sufi elephant and framing -- Other potential traps in decision making -- Evidence based medicine -- Solutions to heuristics, biases, and traps -- Solutions to improve decision-making -- Algorithms and decision trees to aid the decision-making process -- Major Takeaways -- References -- 6 - Spectrum bias in algorithms and artificial intelligence -- Introduction -- Current state of the art -- Spectrum bias and spectrum effect -- Examples of spectrum bias in non-AI diagnostic testing -- Spectrum bias in AI and machine learning algorithms -- Spectrum bias versus overfitting -- Identifying spectrum bias in existing studies -- Issues related to selection of disease spectrum -- Issues related to selection of controls -- Future directions -- Ways to reduce or prevent spectrum bias -- Describing and reporting the disease spectrum -- Subgroup analyses -- Major takeaways -- References -- 7 - Medical visual question answering -- Introduction -- Challenge 1: A trade-off between ambition and practicality -- Challenge 2: Classification versus generation VQA solvers -- Current state of the art -- General domain VQA -- Medical domain VQA -- Future directions -- Main takeaways -- References -- B - Artificial intelligence in cardiovascular areas -- 8 - Artificial intelligence and the electrocardiogram -- Introduction -- Current state of the art -- ECG in cardiac arrhythmia classification -- ECG in cardiovascular disease classification. , ECG in cardiovascular disease risk prediction -- Utilization of ECG in noncardiac disease classification and prediction -- Future directions -- Transfer learning-ready deep learning models for ECG analysis -- Wearable technologies may help facilitate preventive care and address disparities in healthcare access -- Major takeaways -- References -- 9 - Artificial intelligence in electrophysiology -- Introduction -- Current state of the art -- Advancements in basic and computational science -- Emerging advancements in clinical practice -- ECG interpretation -- Atrial fibrillation -- Ventricular arrhythmia -- Cardiac resynchronization therapy -- Future directions -- Major takeaways -- References -- 10 - Artificial intelligence in echocardiography -- Introduction -- Current state of the art -- Machine learning in echocardiography -- Segmentation approaches to highlight objects and boundaries for cardiologists -- Regression analysis for predicting future outcomes -- Classification for automation of acquisition, diagnosis, and prognosis -- Future directions -- Major takeaways -- References -- Further reading -- 11 - Artificial intelligence in cardiac CT -- Introduction -- Current state of the art -- Artificial intelligence (AI) in CCT image preprocessing and quality improvement -- Artificial intelligence (AI) for cardiac structures segmentation -- Artificial Intelligence (AI) for coronary arteries segmentation and atherosclerosis characterization and functional assessment -- Functional assessment of CAD -- Outcome prediction -- Future directions -- Major takeaways -- References -- 12 - Artificial intelligence in cardiac MRI -- Introduction -- Background -- Current state of the art -- Deep learning for MR image acquisition and segmentation -- Deep ANNs for inverse problems in cardiac MRI -- Deep ANNs for cardiac MR image reconstruction. , Deep learning for image generation.

Additional Edition: Print version: Limon, Alfonso Intelligence-Based Cardiology and Cardiac Surgery San Diego : Elsevier Science & Technology,c2023 ISBN 9780323905343

Language: English

UID:

Format: 1 Online-Ressource : , Illustrationen.

ISBN: 978-0-12-823338-2 , 0-12-823338-9

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-12-823337-5

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-12-816462-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 0-12-816462-X

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-12-816743-4

Language: English

DOI: 10.1016/C2020-0-00473-1

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource : , Illustrationen.

ISBN: 978-0-12-823338-2 , 0-12-823338-9

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-12-823337-5

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-12-816462-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 0-12-816462-X

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-12-816743-4

Language: English

DOI: 10.1016/C2020-0-00473-1

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: XXII, 800 S , Ill., graph. Darst , 29 cm

ISBN: 072160692X , 9780721606927

Note: Includes bibliographical references and index

Language: English

Subjects: Medicine

Keywords: Kind ; Heranwachsender ; Herzinsuffizienz ; Herzkrankheit

URL: Inhaltsverzeichnis

UID:

Format: 1 online resource (542 pages)

Edition: 1st ed.

ISBN: 0-323-90629-X

Series Statement: Intelligence-Based Medicine: Subspecialty Series

Content: Intelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine provides an especially timely multidisciplinary and comprehensive survey of artificial intelligence concepts and methodologies. It includes real-life applications in adult and pediatric cardiovascular medicine, spanning the life span from fetus to adult. Led by a senior cardiologist–data scientist and supported by renowned data scientists and cardiac clinicians with an ardent passion for artificial intelligence in cardiovascular medicine, the book provides a clinical interface between the medical and data science domains that is symmetric and realistic.

Note: Front Cover -- Intelligence-Based Cardiology and Cardiac Surgery -- Intelligence-Based Cardiology and Cardiac Surgery -- Copyright -- Dedication -- Contents -- Contributors -- About the editors -- Foreword by Eric Topol -- Foreword by Ami Bhatt -- Preface -- Acknowledgments -- I - Basic concepts of data science and artificial intelligence -- 1 - Introduction to artificial intelligence for cardiovascular clinicians -- Basic concepts of artificial intelligence -- Definitions and concepts -- Artificial intelligence and the neurosciences -- History of artificial intelligence -- Key people and events -- Key epochs and movements -- History of artificial intelligence in medicine -- AI methodologies -- AI adoption -- Healthcare data and databases -- Cardiology data -- Healthcare data management -- Healthcare databases -- The data-to-intelligence continuum and AI -- Machine and deep learning -- Introduction to machine learning -- Classical machine learning and other types of learning -- Supervised learning -- Classification -- Regression -- Unsupervised learning -- Clustering -- Generalization (or dimension reduction) -- Semisupervised and self-supervised learning -- Ensemble learning -- Reinforcement learning -- Neural networks and deep learning -- Perceptron and multilayer perceptrons (MLP) -- Autoencoder neural network -- Generative adversarial network (GAN) -- Convolutional neural network (CNN) -- Recurrent neural network (RNN) -- Transfer learning -- Transformers -- Assessment of model performance -- Assessment methods -- Evaluation of regression models -- Evaluation of classification models -- Fundamental issues in machine and deep learning -- Model and data drift -- Model parameters and hyperparameters -- Imbalanced data set -- Interpretability and explainability -- Bias and variance trade-off -- Fitting of a model -- Curse of dimensionality. , Correlation versus causation -- Machine versus deep learning -- Other key concepts and technologies in artificial intelligence -- Key concepts -- Bias and equity -- Complicated versus complex -- Healthcare cybersecurity -- Health economics -- Ethical considerations -- Education in clinicians -- Legal issues -- Overdiagnosis -- Regulatory issues -- Safety -- Key technologies -- Autonomous systems -- Blockchain -- Cloud technology -- Cognitive computing -- Digital twins -- Edge computing and embedded AI -- Extended reality -- Federated and swarm intelligence -- Foundation model -- Internet of things and everything (IoT and IoE) -- Knowledge graphs -- Low-shot learning -- Metaverse -- Monte Carlo simulation (MCS) -- Natural language processing (NLP) -- Robotics -- Robotic process automation (RPA) -- Human cognition and artificial intelligence in cardiology -- System 1 and system 2 thinking -- Uncertainty in biomedicine -- Clinician cognitive biases and heuristics -- Logical reasoning -- Evidence-based medicine -- Clinician perception/cognition -- Clinician-AI synergy: cognition-based AI -- Current status of AI in medicine and relevance to cardiovascular medicine -- The "why" for intelligence-based cardiology: sanctuary for the perfect storm -- Current areas of AI in medicine with relevance to cardiovascular medicine -- Medical imaging -- Extended reality -- Decision support -- Biomedical diagnostics -- Precision medicine -- Drug discovery -- Digital health -- Wearable technology -- Robotic technology -- Virtual assistance -- Current and future state of AI in cardiology -- Adoption of AI in cardiovascular medicine: the challenges ahead -- References -- II - Artificial intelligence in cardiovascular medicine -- A - Basic concepts of artificial intelligence in cardiology and cardiac surgery. , 2 - Application of artificial intelligence in cardiovascular medicine and cardiac surgery -- Introduction -- Preview of the future -- Why is cardiology special for AI adoption? -- Current state of the art -- Data registries -- Augmented diagnostics, clinical decision support, risk prediction, and treatment -- Precision cardiology -- Potential opportunities for AI implementation in cardiology -- Future directions -- Challenges in implementation of AI in cardiology -- Strategies to overcome challenges in implementation of AI in cardiology -- Major takeaways -- Intelligence-based cardiac care -- References -- Further reading -- 3 - Data and databases in cardiovascular medicine and surgery -- Introduction -- Current state of the art -- Future directions -- Major takeaways -- References -- 4 - Data and databases for pediatric and adult congenital cardiac care -- Introduction -- Current state of the art -- Nomenclature -- Database -- Risk adjustment -- Verification of the completeness and accuracy of the data -- Collaboration across medical and surgical subspecialties -- Linking of databases and registries -- Longitudinal follow-up -- Assessment and improvement of quality -- Future directions -- Improving the discrimination and calibration of risk models with machine learning and other novel approaches -- Minimizing the burden to enter data and thereby minimizing the associated costs -- Documenting longitudinal outcomes consistently and effortlessly -- Measuring the value of healthcare -- Utilizing the massive amount of available data in our cardiac intensive care units with streaming analytics -- Decreasing the costs associated with research and the generation of new knowledge by conducting randomized trials within re ... -- Linking genotypic data to phenotypic data within registries. , Utilizing pediatric and congenital cardiac databases to facilitate personalized precision medicine -- Major takeaways -- References -- 5 - Cognitive biases and heuristics in human cognition -- Introduction -- How do doctors make decisions? -- Our decision making environment -- Impact of culture on decision making -- Asking the right question -- Contrarians who seeing the unseen -- Utility theory and cognitive bias (Kahneman) -- The Sufi elephant and framing -- Other potential traps in decision making -- Evidence based medicine -- Solutions to heuristics, biases, and traps -- Solutions to improve decision-making -- Algorithms and decision trees to aid the decision-making process -- Major Takeaways -- References -- 6 - Spectrum bias in algorithms and artificial intelligence -- Introduction -- Current state of the art -- Spectrum bias and spectrum effect -- Examples of spectrum bias in non-AI diagnostic testing -- Spectrum bias in AI and machine learning algorithms -- Spectrum bias versus overfitting -- Identifying spectrum bias in existing studies -- Issues related to selection of disease spectrum -- Issues related to selection of controls -- Future directions -- Ways to reduce or prevent spectrum bias -- Describing and reporting the disease spectrum -- Subgroup analyses -- Major takeaways -- References -- 7 - Medical visual question answering -- Introduction -- Challenge 1: A trade-off between ambition and practicality -- Challenge 2: Classification versus generation VQA solvers -- Current state of the art -- General domain VQA -- Medical domain VQA -- Future directions -- Main takeaways -- References -- B - Artificial intelligence in cardiovascular areas -- 8 - Artificial intelligence and the electrocardiogram -- Introduction -- Current state of the art -- ECG in cardiac arrhythmia classification -- ECG in cardiovascular disease classification. , ECG in cardiovascular disease risk prediction -- Utilization of ECG in noncardiac disease classification and prediction -- Future directions -- Transfer learning-ready deep learning models for ECG analysis -- Wearable technologies may help facilitate preventive care and address disparities in healthcare access -- Major takeaways -- References -- 9 - Artificial intelligence in electrophysiology -- Introduction -- Current state of the art -- Advancements in basic and computational science -- Emerging advancements in clinical practice -- ECG interpretation -- Atrial fibrillation -- Ventricular arrhythmia -- Cardiac resynchronization therapy -- Future directions -- Major takeaways -- References -- 10 - Artificial intelligence in echocardiography -- Introduction -- Current state of the art -- Machine learning in echocardiography -- Segmentation approaches to highlight objects and boundaries for cardiologists -- Regression analysis for predicting future outcomes -- Classification for automation of acquisition, diagnosis, and prognosis -- Future directions -- Major takeaways -- References -- Further reading -- 11 - Artificial intelligence in cardiac CT -- Introduction -- Current state of the art -- Artificial intelligence (AI) in CCT image preprocessing and quality improvement -- Artificial intelligence (AI) for cardiac structures segmentation -- Artificial Intelligence (AI) for coronary arteries segmentation and atherosclerosis characterization and functional assessment -- Functional assessment of CAD -- Outcome prediction -- Future directions -- Major takeaways -- References -- 12 - Artificial intelligence in cardiac MRI -- Introduction -- Background -- Current state of the art -- Deep learning for MR image acquisition and segmentation -- Deep ANNs for inverse problems in cardiac MRI -- Deep ANNs for cardiac MR image reconstruction. , Deep learning for image generation.

Additional Edition: Print version: Limon, Alfonso Intelligence-Based Cardiology and Cardiac Surgery San Diego : Elsevier Science & Technology,c2023 ISBN 9780323905343

Language: English

UID:

Format: 1 online resource (542 pages)

Edition: 1st ed.

ISBN: 0-323-90629-X

Series Statement: Intelligence-Based Medicine: Subspecialty Series

Content: Intelligence-Based Cardiology and Cardiac Surgery: Artificial Intelligence and Human Cognition in Cardiovascular Medicine provides an especially timely multidisciplinary and comprehensive survey of artificial intelligence concepts and methodologies. It includes real-life applications in adult and pediatric cardiovascular medicine, spanning the life span from fetus to adult. Led by a senior cardiologist–data scientist and supported by renowned data scientists and cardiac clinicians with an ardent passion for artificial intelligence in cardiovascular medicine, the book provides a clinical interface between the medical and data science domains that is symmetric and realistic.

Note: Front Cover -- Intelligence-Based Cardiology and Cardiac Surgery -- Intelligence-Based Cardiology and Cardiac Surgery -- Copyright -- Dedication -- Contents -- Contributors -- About the editors -- Foreword by Eric Topol -- Foreword by Ami Bhatt -- Preface -- Acknowledgments -- I - Basic concepts of data science and artificial intelligence -- 1 - Introduction to artificial intelligence for cardiovascular clinicians -- Basic concepts of artificial intelligence -- Definitions and concepts -- Artificial intelligence and the neurosciences -- History of artificial intelligence -- Key people and events -- Key epochs and movements -- History of artificial intelligence in medicine -- AI methodologies -- AI adoption -- Healthcare data and databases -- Cardiology data -- Healthcare data management -- Healthcare databases -- The data-to-intelligence continuum and AI -- Machine and deep learning -- Introduction to machine learning -- Classical machine learning and other types of learning -- Supervised learning -- Classification -- Regression -- Unsupervised learning -- Clustering -- Generalization (or dimension reduction) -- Semisupervised and self-supervised learning -- Ensemble learning -- Reinforcement learning -- Neural networks and deep learning -- Perceptron and multilayer perceptrons (MLP) -- Autoencoder neural network -- Generative adversarial network (GAN) -- Convolutional neural network (CNN) -- Recurrent neural network (RNN) -- Transfer learning -- Transformers -- Assessment of model performance -- Assessment methods -- Evaluation of regression models -- Evaluation of classification models -- Fundamental issues in machine and deep learning -- Model and data drift -- Model parameters and hyperparameters -- Imbalanced data set -- Interpretability and explainability -- Bias and variance trade-off -- Fitting of a model -- Curse of dimensionality. , Correlation versus causation -- Machine versus deep learning -- Other key concepts and technologies in artificial intelligence -- Key concepts -- Bias and equity -- Complicated versus complex -- Healthcare cybersecurity -- Health economics -- Ethical considerations -- Education in clinicians -- Legal issues -- Overdiagnosis -- Regulatory issues -- Safety -- Key technologies -- Autonomous systems -- Blockchain -- Cloud technology -- Cognitive computing -- Digital twins -- Edge computing and embedded AI -- Extended reality -- Federated and swarm intelligence -- Foundation model -- Internet of things and everything (IoT and IoE) -- Knowledge graphs -- Low-shot learning -- Metaverse -- Monte Carlo simulation (MCS) -- Natural language processing (NLP) -- Robotics -- Robotic process automation (RPA) -- Human cognition and artificial intelligence in cardiology -- System 1 and system 2 thinking -- Uncertainty in biomedicine -- Clinician cognitive biases and heuristics -- Logical reasoning -- Evidence-based medicine -- Clinician perception/cognition -- Clinician-AI synergy: cognition-based AI -- Current status of AI in medicine and relevance to cardiovascular medicine -- The "why" for intelligence-based cardiology: sanctuary for the perfect storm -- Current areas of AI in medicine with relevance to cardiovascular medicine -- Medical imaging -- Extended reality -- Decision support -- Biomedical diagnostics -- Precision medicine -- Drug discovery -- Digital health -- Wearable technology -- Robotic technology -- Virtual assistance -- Current and future state of AI in cardiology -- Adoption of AI in cardiovascular medicine: the challenges ahead -- References -- II - Artificial intelligence in cardiovascular medicine -- A - Basic concepts of artificial intelligence in cardiology and cardiac surgery. , 2 - Application of artificial intelligence in cardiovascular medicine and cardiac surgery -- Introduction -- Preview of the future -- Why is cardiology special for AI adoption? -- Current state of the art -- Data registries -- Augmented diagnostics, clinical decision support, risk prediction, and treatment -- Precision cardiology -- Potential opportunities for AI implementation in cardiology -- Future directions -- Challenges in implementation of AI in cardiology -- Strategies to overcome challenges in implementation of AI in cardiology -- Major takeaways -- Intelligence-based cardiac care -- References -- Further reading -- 3 - Data and databases in cardiovascular medicine and surgery -- Introduction -- Current state of the art -- Future directions -- Major takeaways -- References -- 4 - Data and databases for pediatric and adult congenital cardiac care -- Introduction -- Current state of the art -- Nomenclature -- Database -- Risk adjustment -- Verification of the completeness and accuracy of the data -- Collaboration across medical and surgical subspecialties -- Linking of databases and registries -- Longitudinal follow-up -- Assessment and improvement of quality -- Future directions -- Improving the discrimination and calibration of risk models with machine learning and other novel approaches -- Minimizing the burden to enter data and thereby minimizing the associated costs -- Documenting longitudinal outcomes consistently and effortlessly -- Measuring the value of healthcare -- Utilizing the massive amount of available data in our cardiac intensive care units with streaming analytics -- Decreasing the costs associated with research and the generation of new knowledge by conducting randomized trials within re ... -- Linking genotypic data to phenotypic data within registries. , Utilizing pediatric and congenital cardiac databases to facilitate personalized precision medicine -- Major takeaways -- References -- 5 - Cognitive biases and heuristics in human cognition -- Introduction -- How do doctors make decisions? -- Our decision making environment -- Impact of culture on decision making -- Asking the right question -- Contrarians who seeing the unseen -- Utility theory and cognitive bias (Kahneman) -- The Sufi elephant and framing -- Other potential traps in decision making -- Evidence based medicine -- Solutions to heuristics, biases, and traps -- Solutions to improve decision-making -- Algorithms and decision trees to aid the decision-making process -- Major Takeaways -- References -- 6 - Spectrum bias in algorithms and artificial intelligence -- Introduction -- Current state of the art -- Spectrum bias and spectrum effect -- Examples of spectrum bias in non-AI diagnostic testing -- Spectrum bias in AI and machine learning algorithms -- Spectrum bias versus overfitting -- Identifying spectrum bias in existing studies -- Issues related to selection of disease spectrum -- Issues related to selection of controls -- Future directions -- Ways to reduce or prevent spectrum bias -- Describing and reporting the disease spectrum -- Subgroup analyses -- Major takeaways -- References -- 7 - Medical visual question answering -- Introduction -- Challenge 1: A trade-off between ambition and practicality -- Challenge 2: Classification versus generation VQA solvers -- Current state of the art -- General domain VQA -- Medical domain VQA -- Future directions -- Main takeaways -- References -- B - Artificial intelligence in cardiovascular areas -- 8 - Artificial intelligence and the electrocardiogram -- Introduction -- Current state of the art -- ECG in cardiac arrhythmia classification -- ECG in cardiovascular disease classification. , ECG in cardiovascular disease risk prediction -- Utilization of ECG in noncardiac disease classification and prediction -- Future directions -- Transfer learning-ready deep learning models for ECG analysis -- Wearable technologies may help facilitate preventive care and address disparities in healthcare access -- Major takeaways -- References -- 9 - Artificial intelligence in electrophysiology -- Introduction -- Current state of the art -- Advancements in basic and computational science -- Emerging advancements in clinical practice -- ECG interpretation -- Atrial fibrillation -- Ventricular arrhythmia -- Cardiac resynchronization therapy -- Future directions -- Major takeaways -- References -- 10 - Artificial intelligence in echocardiography -- Introduction -- Current state of the art -- Machine learning in echocardiography -- Segmentation approaches to highlight objects and boundaries for cardiologists -- Regression analysis for predicting future outcomes -- Classification for automation of acquisition, diagnosis, and prognosis -- Future directions -- Major takeaways -- References -- Further reading -- 11 - Artificial intelligence in cardiac CT -- Introduction -- Current state of the art -- Artificial intelligence (AI) in CCT image preprocessing and quality improvement -- Artificial intelligence (AI) for cardiac structures segmentation -- Artificial Intelligence (AI) for coronary arteries segmentation and atherosclerosis characterization and functional assessment -- Functional assessment of CAD -- Outcome prediction -- Future directions -- Major takeaways -- References -- 12 - Artificial intelligence in cardiac MRI -- Introduction -- Background -- Current state of the art -- Deep learning for MR image acquisition and segmentation -- Deep ANNs for inverse problems in cardiac MRI -- Deep ANNs for cardiac MR image reconstruction. , Deep learning for image generation.

Additional Edition: Print version: Limon, Alfonso Intelligence-Based Cardiology and Cardiac Surgery San Diego : Elsevier Science & Technology,c2023 ISBN 9780323905343

Language: English

UID:

Format: 1 Online-Ressource : , Illustrationen.

ISBN: 978-0-12-823338-2 , 0-12-823338-9

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-12-823337-5

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-12-816462-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 0-12-816462-X

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-12-816743-4

Language: English

DOI: 10.1016/C2020-0-00473-1

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (xviii, 592 Seiten) : , Illustrationen.

Edition: Third edition

ISBN: 978-0-443-25030-9

Series Statement: Diagnostic pathology

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-443-25008-8

Language: English

UID:

Format: 1 online resource (xxvi, 521 pages)

ISBN: 0-12-823338-9

Note: Machine generated contents note: Introduction to artificial intelligence -- , Basic Concepts of Artificial Intelligence -- , Definitions -- , Types of Artificial Intelligence -- , Artificial Intelligence and Data Science -- , The Human -- Machine Intelligence Continuum -- , The Analytics Continuum -- , Artificial Intelligence and the Neurosciences -- , The Doctor's Brain and Machine Intelligence -- , References -- , History of Artificial Intelligence -- , Key People and Events -- , Alan Turing and the Turing Machine -- , The Dartmouth Conference -- , Rosenblatt's Perceptron -- , Key Epochs and Movements -- , Good Old-fashioned Artificial Intelligence -- , Computational Intelligence -- , The Artificial Intelligence Winters -- , References -- , History of Artificial Intelligence in Medicine -- , Rule-based Expert Systems -- , Other Artificial Intelligence Methodologies -- , Failure of Adoption -- , Ten Common Misconceptions of Artificial Intelligence in Medicine -- , References -- , Key Concepts -- , Data science and artificial intelli' gence in the current era -- , Health-care Data and Databases -- , Health-care Data -- , Big Data -- , Health-care Data Conundrum -- , Health-care Data Management -- , Data Processing and Storage -- , Electronic Medical Record Adoption and Interoperability -- , Health-care Databases -- , Database-management Systems -- , Relational Database -- , Object-oriented Database -- , Graph Database -- , The Data-to-intelligence Continuum and Artificial Intelligence -- , References -- , Machine and Deep Learning -- , Introduction to Machine Learning -- , Data Mining and Knowledge Discovery -- , History and Current State of Machine Learning -- , Machine Learning versus Conventional Programing -- , Machine Learning Workflow -- , Data Science in Biomedicine -- , Programing Languages in Biomedical Data Science -- , Classical Machine Learning -- , Supervised Learning -- , Unsupervised Learning -- , Ensemble Learning -- , Reinforcement Learning -- , Neural Networks and Deep Learning -- , Perceptron and Multilayer Perceptrons -- , Deep Learning -- , Autoencoder Neural Network -- , Assessment of Model Performance -- , Assessment Methods -- , Evaluation of Regression Models -- , Evaluation of Classification Models -- , Fundamental Issues in Machine and Deep Learning -- , Interpretability and Explainability -- , Bias and Variance Trade-off -- , Fitting -- , Curse of Dimensionality -- , Correlation versus Causation -- , Machine versus Deep Learning -- , References -- , Other Key Concepts in Artificial Intelligence -- , Cognitive Computing -- , Natural Language Processing -- , Robotics -- , Autonomous Systems -- , Robotic Process Automation -- , Other Key Technologies Related to Artificial Intelligence -- , Augmented and virtual reality -- , Blockchain -- , Cloud -- , Cybersecurity -- , Internet of Things -- , Key Issues Related to Artificial Intelligence -- , Bias -- , Ethics -- , Safety -- , Legal -- , Key Concepts -- , Ten Questions to Assess your Data Science and Artificial Intelligence Knowledge -- , Ten Steps to Become More Knowledgeable in Artificial Intelligence in Medicine -- , References -- , The current era of artificial intelligence in medicine -- , Clinician Cognition and Artificial Intelligence in Medicine -- , The Rationale for Intelligence-based Medicine -- , Adoption of Artificial Intelligence in Medicine: The Challenges Ahead -- , Clinician Cognition and Artificial Intelligence in Medicine -- , Current Artificial Intelligence in Medicine Applications -- , References -- , Artificial Intelligence in Subspecialties -- , The Present State of Artificial Intelligence in Subspecialties -- , The subspecialties and artificial intelligence strategy and applications -- , Artificial Intelligence Applications for all Subspecialties -- , Anesthesiology -- , Cardiology (Adult and Pediatric) and Cardiac Surgery -- , Critical Care Medicine -- , Dermatology -- , Emergency Medicine -- , Endocrinology -- , Gastroenterology -- , Global and Public Health/Epidemiology -- , Hematology -- , Infectious Disease -- , Internal and Family Medicine/Primary Care -- , Nephrology -- , Neurosciences (Neurology/Neurosurgery and Psychiatry/Psychology) -- , Obstetrics/Gynecology -- , Oncology -- , Ophthalmology -- , Pathology -- , Pediatrics -- , Pulmonology -- , Radiology -- , Rheumatology -- , Surgery -- , Dentistry -- , Digital Health -- , Genomic Medicine and Precision/Personalized Medicine -- , Physical Medicine and Rehabilitation -- , Regenerative Medicine -- , Veterinary Medicine -- , Medical Education and Training -- , Nursing -- , Healthcare Administration -- , References -- , Implementation of Artificial Intelligence in Medicine -- , Key Concepts -- , Assessment of Artificial Intelligence Readiness in Health-care Organizations -- , Ten Elements for Successful Implementation of Artificial Intelligence in Medicine -- , Ten Obstacles to Overcome for Implementation of Artificial Intelligence in Medicine -- , References -- , The future of artificial intelligence and application in medicine -- , Key Concepts of the Future of Artificial Intelligence -- , 5G -- , Augmented and Virtual Reality -- , Blockchain and Cybersecurity -- , Brain -- Computer Interface -- , Capsule Network -- , Cloud Artificial Intelligence -- , Edge Computing -- , Embedded Artificial Intelligence (or Internet of Everything) -- , Fuzzy Cognitive Maps -- , Generative Query Network -- , Hypergraph Database -- , Low-shot Learning -- , Neuromorphic Computing -- , Quantum Computing -- , Recursive Cortical Network -- , Spiking Neural Network (SNN) -- , Swarm Intelligence -- , Temporal Convolutional Nets -- , Transfer Learning -- , Data and Databases -- , References -- , The Future of Artificial Intelligence in Medicine -- , Key Concepts -- , References.

Additional Edition: ISBN 0-12-823337-0

Language: English