http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Machine learning in cardiovascular medicine
(Associate professor of mechanical and nuclear engineering)
1 online resource : illustrations (black and white, and colour)
Machine Learning in Cardiovascular Medicine addresses the ever-expanding applications of artificial intelligence (AI), specifically machine learning (ML), in healthcare and within cardiovascular medicine.
Front Cover -- Machine Learning in Cardiovascular Medicine -- Machine Learning in Cardiovascular Medicine -- Copyright -- Dedication -- Contents -- Contributors -- Preface -- Acknowledgments -- Nomenclature -- Introduction -- 1 -- Technological advances within digital medicine -- Introduction -- Wearable sensing devices -- Fitness trackers and smartwatches -- Introduction -- Devices and sensors -- Clinical applications -- Wearable cardioverter defibrillator (WCD) -- Introduction -- How does the WCD function? -- What are the indications for WCD? -- Does the WCD technology save lives? -- Mobile health in cardiovascular medicine -- Introduction -- Smartphone-based electrocardiogram (EKG) -- Pocket-echocardiography -- Robotic technology in cardiology -- Robotics in PCI -- Robotics in PVI -- Robotics in electrophysiology -- Future perspectives -- Conclusions -- References -- 2 -- An overview of artificial intelligence: basics and state-of-the-art algorithms -- Introduction -- Basic concepts -- What are AI, machine learning, and deep learning? And what is the difference between them? -- Types of machine learning -- Machine learning in practice -- Generalization in machine learning: overfitting versus underfitting -- Dataset split: training versus validation versus testing -- Basic evaluation metrics -- State-of-the-art algorithms -- Bayesian machine learning -- Decision tree -- Decision tree versus random forest -- Random forest versus boosting -- Regression -- Linear regression versus logistic regression -- Support vector machine -- Deep learning and neural networks -- Why neural networks work? -- Typical deep learning pipeline -- Neural network versus convolutional neural network -- Convolutional neural network layers -- Convolutional neural network architectures -- Attention model and interpretability of neural networks -- Conclusions -- References.
3 -- Machine learning for predictive analytics -- Introduction -- Developing a predictive model -- Defining the opportunity -- Creating the dataset -- Developing a model -- Evaluating model performance -- Machine learning applications in cardiovascular medicine -- Preventive care -- Heart failure -- Interventional cardiology -- Electrophysiology -- Challenges and opportunities -- Data quality -- Generalizability -- Bias -- Interpretability -- Conclusions -- References -- 4 -- Deep learning for biomedical applications -- Introduction -- A brief overview on deep learning -- Deep learning for healthcare -- Clinical imaging -- Electronic health records -- Biological data -- Digital health monitoring devices -- Discussion -- References -- 5 -- Generative adversarial network for cardiovascular imaging -- Introduction -- Research background -- Generative adversarial networks -- Challenges for GAN optimization -- Loss-variant GANs -- Architecture-variant GANs -- GAN evaluation -- Deep learning in cardiac medical imaging -- GAN application in medical imaging -- Medical image synthesis -- Classification and detection -- Segmentation -- Datasets, challenges, and current public benchmarks -- Challenges, limitations, and future perspectives -- References -- 6 -- Natural language processing -- Introduction to biomedical natural language processing -- Tasks in biomedical NLP -- High-level tasks -- Named-entity recognition -- Entity linking -- Relationship extraction -- Low-level tasks -- Tokenization -- Sentence boundary detection -- Part-of-speech tagging -- Syntactic parsing -- NLP methods -- Rule-based methods -- Statistical and machine learning methods -- Features -- Features -- n-Gram language model -- n-Gram language model -- Hidden Markov model -- Hidden Markov model -- Support vector machine -- Support vector machine -- Neural networks -- Neural networks.
Biomedical NLP resources and systems -- Biomedical terminologies and ontologies -- The Unified Medical Language System -- RxNorm -- Biomedical NLP systems -- MetaMap and SemRep -- cTAKES -- The BioMedical information collection and understanding system -- CLAMP (clinical language annotation, modeling, and processing toolkit) -- Summary -- Applications of NLP in cardiovascular research -- NLP for information extraction -- Rule-based NLP applications in cardiovascular research -- Machine learning and deep learning with NLP applications in research -- NLP for clinical decision support -- NLP for predictive models related to cardiovascular diseases -- NLP for cohort identification of cardiovascular diseases -- Conclusion -- References -- 7 -- Contemporary advances in medical imaging -- Introduction -- Echocardiography -- History of echocardiography -- Contrast agents in echocardiography -- Three-dimensional echocardiography -- Echocardiography strain imaging -- Nuclear cardiology -- Single-photon emission computed tomography -- Position emission tomography -- Technetium-99m pyrophosphate imaging -- Cardiac computed tomography -- Cardiac CT assessment of coronary artery disease -- CT fractional flow reserve -- CT perfusion -- Cardiac CT in structural heart disease -- Cardiac magnetic resonance -- T1 mapping and late-gadolinium enhancement -- CMR stress perfusion -- Valvular heart disease -- CMR 4D flow -- Conclusions -- References -- 8 -- Ultrasound and artificial intelligence -- Introduction -- Cardiovascular/echocardiography -- Transthoracic echocardiography and transesophageal echocardiography -- Quality assessment and view classification -- Left ventricle measurements -- Disease classification -- Intracardiac echocardiography -- Intravascular ultrasound -- Breast, liver, and thyroid ultrasound -- Breast ultrasound -- Liver ultrasound.
Thyroid ultrasound -- Discussion and outlook -- References -- 9 -- Computed tomography and artificial intelligence -- Introduction -- Terminology -- Artificial intelligence in CT image reconstruction -- Deep learning approaches -- Denoising low-dose CT images -- Improving sparse-view CT reconstruction -- Removing image artifacts -- Artificial intelligence in cardiac CT image analysis -- Image segmentation -- Semiautomated plaque analysis -- Automated measurements -- Outcome prediction -- Cardiac CT radiomics -- Artificial intelligence in CT brain for stroke -- Noncontrast CT -- CT angiography -- CT perfusion -- Artificial intelligence in CT for cancer screening -- Lung nodule detection -- Identification of high-risk imaging features -- Summary -- References -- Further reading -- 10 -- Magnetic resonance imaging and artificial intelligence -- Introduction -- Image reconstruction with AI -- Role of AI in cardiac MR analysis -- Left and right ventricular function -- Role of AI in tissue characterization -- AI for prognostic assessment -- Limitation and future directions -- Conclusions -- References -- 11 -- Nuclear imaging and artificial intelligence -- Artificial intelligence in cardiac SPECT and PET image analysis -- Nonlearning expert systems -- Traditional machine learning -- Shallow artificial neural networks -- Deep learning and convolutional neural networks -- Artificial intelligence in cardiac SPECT and PET image reconstruction -- End-to-end reconstruction -- Attenuation correction -- Motion correction -- Denoising and image quality improvement -- Summary -- References -- 12 -- Radiomics in cardiovascular imaging: principles and clinical implications -- Introduction -- Concept of radiomics -- Process of radiomics -- Image acquisition -- Identification of the region of interest and segmentation -- Extraction of radiomic features.
Intensity-based metrics -- Texture-based metrics -- Second-order statistics -- Second-order statistics -- Higher-order statistics -- Higher-order statistics -- Shape-based metrics -- One-, two-, and three-dimensional metrics -- One-, two-, and three-dimensional metrics -- Fractal dimension -- Fractal dimension -- Transform-based metrics -- Fourier and Gabor transforms -- Fourier and Gabor transforms -- Wavelet transforms -- Wavelet transforms -- Database building -- Modeling -- Clinical implications -- Characterization of myocardial texture -- Automated coronary artery calcium scoring -- Coronary plaque phenotyping -- Radiomic phenotyping of perivascular fat -- Valvular heart disease -- Intracardiac mass -- Risk stratification -- The future direction of radiomic research -- Conclusions -- References -- 13 -- Automated interpretation of electrocardiographic tracings -- Electrocardiography -- Traditional approach to automated ECG analysis -- End-to-end automated ECG analysis -- Evaluation of ECG classifiers -- Research directions -- Conclusions -- References -- 14 -- Machine learning in cardiovascular genomics, proteomics, and drug discovery -- Introduction -- Machine learning in genomics -- Molecular genetics -- Population and complex genetics -- Machine learning in proteomics -- Machine learning in drug discovery -- Summary -- References -- 15 -- Wearable devices and machine learning algorithms for cardiovascular health assessment -- Introduction -- Types of data collected -- Machine learning (ML) -- Uses of smart wearable data -- Lifestyle management in the general population -- Screening for CVD -- Disease management in CVD patients -- Management of atrial fibrillation -- Detection of ST-elevation myocardial infarction (STEMI) -- Postmyocardial infarction management and recovery -- Detection of hyperkalemia in heart failure patients.
edited by Subhi J. Al'Aref, Gurpreet Singh, Lohendran Baskaran, Dimitri Metaxas.
Includes bibliographical references and index.