Details

Predicting Heart Failure


Predicting Heart Failure

Invasive, Non-Invasive, Machine Learning, and Artificial Intelligence Based Methods
1. Aufl.

von: Kishor Kumar Sadasivuni, Hassen M. Ouakad, Somaya Al-Maadeed, Huseyin C. Yalcin, Issam Bait Bahadur

145,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 05.04.2022
ISBN/EAN: 9781119813033
Sprache: englisch
Anzahl Seiten: 352

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Beschreibungen

<b>PREDICTING HEART FAILURE</b> <p><i>Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods</i> focuses on the mechanics and symptoms of heart failure and various approaches, including conventional and modern techniques to diagnose it. <p>This book also provides a comprehensive but concise guide to all modern cardiological practice, emphasizing practical clinical management in many different contexts. <i>Predicting Heart Failure</i> supplies readers with trustworthy insights into all aspects of heart failure, including essential background information on clinical practice guidelines, in-depth, peer-reviewed articles, and broad coverage of this fast-moving field. Readers will also find: <ul><li>Discussion of the main characteristics of cardiovascular biosensors, along with their open issues for development and application</li> <li>Summary of the difficulties of wireless sensor communication and power transfer, and the utility of artificial intelligence in cardiology</li> <li>Coverage of data mining classification techniques, applied machine learning and advanced methods for estimating HF severity and diagnosing and predicting heart failure</li> <li>Discussion of the risks and issues associated with the remote monitoring system</li> <li>Assessment of the potential applications and future of implantable and wearable devices in heart failure prediction and detection </li> <li>Artificial intelligence in mobile monitoring technologies to provide clinicians with improved treatment options, ultimately easing access to healthcare by all patient populations.</li></ul> <p> Providing the latest research data for the diagnosis and treatment of heart failure, <i>Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods</i> is an excellent resource for nurses, nurse practitioners, physician assistants, medical students, and general practitioners to gain a better understanding of bedside cardiology.
<p>Preface vii</p> <p>Abbreviations ix</p> <p>Acknowledgment xvii</p> <p><b>1 Invasive, Non-Invasive, Machine Learning, and Artificial Intelligence Based Methods for Prediction of Heart Failure </b><b>1<br /></b><i>Hidayet Takcı</i></p> <p><b>2 Conventional Clinical Methods for Predicting Heart Disease </b><b>23<br /></b><i>Aisha A-Mohannadi, Jayakanth Kunhoth, Al Anood Najeeb, Somaya Al-Maadeed, and Kishor Kumar Sadasivuni</i></p> <p><b>3 Types of Biosensors and their Importance in Cardiovascular Applications </b><b>47<br /></b><i>S Irem Kaya, Leyla Karadurmuş, Ahmet Cetinkaya, Goksu Ozcelikay, and Sibel A Ozkan</i></p> <p><b>4 Overview and Challenges of Wireless Communication and Power Transfer for Implanted Sensors </b><b>81<br /></b><i>Mohamed Zied Chaari and Somaya Al-Maadeed</i></p> <p><b>5 Minimally Invasive and Non-Invasive Sensor Technologies for Predicting Heart Failure: An Overview </b><b>109<br /></b><i>Huseyin Enes Salman, Mahmoud Khatib A.A Al-Ruweidi, Hassen M Ouakad, and Huseyin C Yalcin</i></p> <p><b>6 Artificial Intelligence Techniques in Cardiology: An Overview </b><b>139<br /></b><i>Ikram-Ul Haq and Bo Xu</i></p> <p><b>7 Utilizing Data Mining Classification Algorithms for Early Diagnosis of Heart Diseases </b><b>155<br /></b><i>Ahmad Mousa Altamimi and Mohammad Azzeh</i></p> <p><b>8 Applications of Machine Learning for Predicting Heart Failure </b><b>171<br /></b><i>Sabri Boughorbel, Yassine Himeur, Huseyin Enes Salman, Faycal Bensaali,Faisal Farooq, and Huseyin C Yalcin</i></p> <p><b>9 Machine Learning Techniques for Predicting and Managing Heart Failure </b><b>189<br /></b><i>Dafni K Plati, Evanthia E Tripoliti, Georgia S Karanasiou, Aidonis Rammos,</i><i>Aris Bechlioulis, Chris J Watson, Ken McDonald, Mark Ledwidge, Yorgos Goletsis, Katerina K Naka, and Dimitrios I Fotiadis</i></p> <p><b>10 Clinical Applications of Artificial Intelligence in Early and Accurate Detection of Low- Concentration CVD Biomarkers </b><b>227<br /></b><i>Meena Laad, Sajna M.S, Kishor Kumar Sadasivuni, and Sadiya Waseem</i></p> <p><b>11 Commercial Non-Invasive and Invasive Devices for Heart Failure Prediction: A Review <i>243<br /></i></b><i>Jayakanth Kunhoth, Nandhini Subramanian, and Ahmed Bouridane</i></p> <p><b>12 Artificial Intelligence Based Commercial Non-Invasive and Invasive Devices for Heart Failure Diagnosis and Prediction </b><b>269<br /></b><i>Kanchan Kulkarni, Eric M Isselbacher, and Antonis A Armoundas</i></p> <p><b>13 Future Techniques and Perspectives on Implanted and Wearable Heart </b><b>Failure Detection Devices </b><b>295<br /></b><i>Muhammad E.H Chowdhury, Amith Khandaker, Yazan Qiblawey, Fahmida Haque, </i><i>Maymouna Ezeddin, Tawsifur Rahman, Nabil Ibtehaz, and Khandaker Reajul Islam</i></p> <p>Index 321</p>
<p><b>About the Editors</b></p> <p><b>Dr Kishor Kumar Sadasivuni,</b> Center for Advanced Materials, Qatar University, Qatar <p><b>Dr Hassen M. Ouakad,</b> Department of Mechanical and Industrial Engineering, Sultan Qaboos University, Oman <p><b>Prof. Somaya Al-Maadeed,</b> Department of Computer Science and Engineering, Qatar University, Qatar <p><b>Dr Huseyin C. Yalcin, Biomedical Research Center, Qatar University, Qatar <p><b>Dr Issam Bait Bahadur,</b> Department of Mechanical and Industrial Engineering, Sultan Qaboos University, Oman <p>This publication was supported by Qatar University Internal Grant No. IRCC-2020-013 and Sultan Qaboos University through Grant # CL/SQU-QU/ENG/20/01, respectively. The findings achieved herein are solely the responsibility of the authors.
<p><i>Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods</i> focuses on the mechanics and symptoms of heart failure and various approaches, including conventional and modern techniques to diagnose it. </p> <p>This book also provides a comprehensive but concise guide to all modern cardiological practice, emphasizing practical clinical management in many different contexts. <i>Predicting Heart Failure</i> supplies readers with trustworthy insights into all aspects of heart failure, including essential background information on clinical practice guidelines, in-depth, peer-reviewed articles, and broad coverage of this fast-moving field. Readers will also find: <ul><li>Discussion of the main characteristics of cardiovascular biosensors, along with their open issues for development and application</li> <li>Summary of the difficulties of wireless sensor communication and power transfer, and the utility of artificial intelligence in cardiology</li> <li>Coverage of data mining classification techniques, applied machine learning and advanced methods for estimating HF severity and diagnosing and predicting heart failure</li> <li>Discussion of the risks and issues associated with the remote monitoring system</li> <li>Assessment of the potential applications and future of implantable and wearable devices in heart failure prediction and detection </li> <li>Artificial intelligence in mobile monitoring technologies to provide clinicians with improved treatment options, ultimately easing access to healthcare by all patient populations.</li></ul> <p> Providing the latest research data for the diagnosis and treatment of heart failure, <i>Predicting Heart Failure: Invasive, Non-Invasive, Machine Learning and Artificial Intelligence Based Methods</i> is an excellent resource for nurses, nurse practitioners, physician assistants, medical students, and general practitioners to gain a better understanding of bedside cardiology.

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