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AI-driven innovation in healthcare d...

Polat, Olcay, (1983-)

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AI-driven innovation in healthcare data analytics

Record Type:	Electronic resources : Monograph/item
Title/Author:	AI-driven innovation in healthcare data analytics/ Leyla Özgür Polat, Olcay Polat, editors.
remainder title:	Artificial intelligence driven innovation in healthcare data analytics
other author:	Polat, Olcay,
Published:	Hershey, Pennsylvania :IGI Global Scientific Publishing, : 2025.,
Description:	1 online resource (xxi, 494 p.) :ill. (some col.)
[NT 15003449]:	Preface -- Chapter 1. AI in Healthcare Data Analytics Trends and Transformative Innovations -- Chapter 2. Transforming Healthcare With AI and Machine Learning: Applications and Impacts -- Chapter 3. Leveraging AI and Machine Learning for Next-Generation Clinical Decision Support Systems (CDSS) -- Chapter 4. The X-Factor of Healthcare: How Explainable AI Is Revolutionizing Medical Practice -- Chapter 5. Clinical Decision Support Systems Using Machine Learning: A Case Study on Thyroid Disease Prediction -- Chapter 6. Machine Learning in Health: A Study on Heart Disease Prediction -- Chapter 7. COVID Detection Model Using X-Ray Images: Role of Data Science and Machine Learning in Digital Medical System -- Chapter 8. Optimizing Healthcare Operations With AI Algorithms by Enhancing Skin Cancer Diagnosis Using Advanced Image Processing and Classification Techniques -- Chapter 9. AI-Driven-IoT(AIIoT)-Based Decision Making in Kidney Diseases Patient Healthcare Monitoring: KSK Approach for Kidney Monitoring -- Chapter 10. Analysis of Insomnia for Old Aged People Using Machine Learning Algorithms -- Chapter 11. A Contactless Real-Time System to Classify Multi-Class Sitting Posture Using Depth Sensor-Based Data -- Chapter 12. Harnessing Machine Learning and Deep Learning in Healthcare From Early Diagnosis to Personalized Treatment: Comprehensive Approach of Deep Learning In Healthcare -- Chapter 13. Harnessing AI for Better Health Outcomes: Emerging Trends -- Compilation of References -- About the Contributors -- Index.
Subject:	Artificial intelligence - Medical applications. -
Online resource:	https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-7277-7
ISBN:	9798369372791

AI-driven innovation in healthcare data analytics
AI-driven innovation in healthcare data analytics[electronic resource] /Artificial intelligence driven innovation in healthcare data analyticsLeyla Özgür Polat, Olcay Polat, editors. - Hershey, Pennsylvania :IGI Global Scientific Publishing,2025. - 1 online resource (xxi, 494 p.) :ill. (some col.) - Advances in healthcare information systems and administration (AHISA) book series. - Advances in healthcare information systems and administration (AHISA) book series..

Includes bibliographical references and index.

Preface -- Chapter 1. AI in Healthcare Data Analytics Trends and Transformative Innovations -- Chapter 2. Transforming Healthcare With AI and Machine Learning: Applications and Impacts -- Chapter 3. Leveraging AI and Machine Learning for Next-Generation Clinical Decision Support Systems (CDSS) -- Chapter 4. The X-Factor of Healthcare: How Explainable AI Is Revolutionizing Medical Practice -- Chapter 5. Clinical Decision Support Systems Using Machine Learning: A Case Study on Thyroid Disease Prediction -- Chapter 6. Machine Learning in Health: A Study on Heart Disease Prediction -- Chapter 7. COVID Detection Model Using X-Ray Images: Role of Data Science and Machine Learning in Digital Medical System -- Chapter 8. Optimizing Healthcare Operations With AI Algorithms by Enhancing Skin Cancer Diagnosis Using Advanced Image Processing and Classification Techniques -- Chapter 9. AI-Driven-IoT(AIIoT)-Based Decision Making in Kidney Diseases Patient Healthcare Monitoring: KSK Approach for Kidney Monitoring -- Chapter 10. Analysis of Insomnia for Old Aged People Using Machine Learning Algorithms -- Chapter 11. A Contactless Real-Time System to Classify Multi-Class Sitting Posture Using Depth Sensor-Based Data -- Chapter 12. Harnessing Machine Learning and Deep Learning in Healthcare From Early Diagnosis to Personalized Treatment: Comprehensive Approach of Deep Learning In Healthcare -- Chapter 13. Harnessing AI for Better Health Outcomes: Emerging Trends -- Compilation of References -- About the Contributors -- Index.

"As the healthcare industry continues to rely on data to enhance patient outcomes and streamline operations, artificial intelligence (AI) becomes a powerful tool for complex dataset analysis using improved speed and accuracy. From predictive modeling in disease outbreak management to personalized treatment plans for individual patient profiles, AI technologies are reshaping clinical decision-making and resource allocation. Harnessing the potential of machine learning and advanced analytics may allow healthcare providers to uncover insights that drive innovation, improve patient care, and optimize operational efficiency. AI-Driven Innovation in Healthcare Data Analytics explores the intersection of AI and healthcare data analytics. It examines the application of AI-driven techniques, including machine learning, deep learning, and data mining, in addressing complex challenges in healthcare management. This book covers topics such as data science, medical diagnosis, and patient care, and is a useful resource for healthcare professionals, data scientists, computer engineers, business owners, academicians, and researchers."--

Mode of access: World Wide Web.

ISBN: 9798369372791Subjects--Topical Terms:

900591
Artificial intelligence
--Medical applications.Subjects--Index Terms:

Artificial Intelligence.Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: R859.7.A78 / A33 2025eb

Dewey Class. No.: 610.285/63

National Library of Medicine Call No.: WM 26.55.A7 / A33 2025eb

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505 0 $a Preface -- Chapter 1. AI in Healthcare Data Analytics Trends and Transformative Innovations -- Chapter 2. Transforming Healthcare With AI and Machine Learning: Applications and Impacts -- Chapter 3. Leveraging AI and Machine Learning for Next-Generation Clinical Decision Support Systems (CDSS) -- Chapter 4. The X-Factor of Healthcare: How Explainable AI Is Revolutionizing Medical Practice -- Chapter 5. Clinical Decision Support Systems Using Machine Learning: A Case Study on Thyroid Disease Prediction -- Chapter 6. Machine Learning in Health: A Study on Heart Disease Prediction -- Chapter 7. COVID Detection Model Using X-Ray Images: Role of Data Science and Machine Learning in Digital Medical System -- Chapter 8. Optimizing Healthcare Operations With AI Algorithms by Enhancing Skin Cancer Diagnosis Using Advanced Image Processing and Classification Techniques -- Chapter 9. AI-Driven-IoT(AIIoT)-Based Decision Making in Kidney Diseases Patient Healthcare Monitoring: KSK Approach for Kidney Monitoring -- Chapter 10. Analysis of Insomnia for Old Aged People Using Machine Learning Algorithms -- Chapter 11. A Contactless Real-Time System to Classify Multi-Class Sitting Posture Using Depth Sensor-Based Data -- Chapter 12. Harnessing Machine Learning and Deep Learning in Healthcare From Early Diagnosis to Personalized Treatment: Comprehensive Approach of Deep Learning In Healthcare -- Chapter 13. Harnessing AI for Better Health Outcomes: Emerging Trends -- Compilation of References -- About the Contributors -- Index.
520 3 $a "As the healthcare industry continues to rely on data to enhance patient outcomes and streamline operations, artificial intelligence (AI) becomes a powerful tool for complex dataset analysis using improved speed and accuracy. From predictive modeling in disease outbreak management to personalized treatment plans for individual patient profiles, AI technologies are reshaping clinical decision-making and resource allocation. Harnessing the potential of machine learning and advanced analytics may allow healthcare providers to uncover insights that drive innovation, improve patient care, and optimize operational efficiency. AI-Driven Innovation in Healthcare Data Analytics explores the intersection of AI and healthcare data analytics. It examines the application of AI-driven techniques, including machine learning, deep learning, and data mining, in addressing complex challenges in healthcare management. This book covers topics such as data science, medical diagnosis, and patient care, and is a useful resource for healthcare professionals, data scientists, computer engineers, business owners, academicians, and researchers."-- $c Provided by publisher.
538 $a Mode of access: World Wide Web.
650 0 $a Artificial intelligence $x Medical applications. $3 900591
650 0 $a Medical informatics. $3 661258
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653 $a Artificial Intelligence.
653 $a Data Science.
653 $a Decision Support Systems.
653 $a Disease Management.
653 $a Healthcare Algorithms.
653 $a Machine Learning.
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653 $a Medical Technology.
653 $a Patient Care.
653 $a Personalized Treatment.
653 $a Predictive Analytics.
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700 1 $a Polat, Olcay, $d 1983- $3 3793284
700 1 $a Polat, Leyla Özgür. $3 3793294
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776 0 8 $i Print version: $z 9798369372777
830 0 $a Advances in healthcare information systems and administration (AHISA) book series. $3 3510601
856 4 0 $u https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-7277-7