Synopsis
The landscape of healthcare is transformed by the integration of advanced data analytics, especially in the realm of multi-source data analysis. By combining diverse datasets, such as electronic health records (EHRs), genetic information, wearable device data, and patient-reported outcomes, healthcare providers can gain a comprehensive understanding of a patient's health status. This approach creates more personalized treatment plans, enhances diagnostic accuracy, and supports early detection of potential health issues. Communication between various data sources allows for the identification of hidden trends and patterns, improving predictive capabilities and optimizing patient outcomes. As healthcare systems adopt this data-driven process, it is crucial to address challenges related to data privacy, integration, and the interpretation of complex datasets, ensuring the potential benefits of multi-source data analysis are realized in ethical and effective ways. Optimizing Patient Outcomes Through Multi-Source Data Analysis in Healthcare explores the transformative potential of big data and AI in healthcare, focusing on informed decision-making. It delves into the integration of vast, diverse datasets, analyzed through AI algorithms to enhance patient outcomes and operational efficiency. This book covers topics such as automation, machine learning, and neural networks, and is a useful resource for healthcare professionals, computer engineers, business owners, academicians, researchers, and data scientists.
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About the Authors
Ferdin Joe John Joseph, PhD, is currently affiliated with Thai-Nichi Institute of Technology.
Karthikeyan Chinnusamy is Sr Principal with more than 25 years of experience in IT, Product Dev, R&D and Education fields. Fellow IETE, Fellow IE, Sr Member IEEE, Sr Member ACM, Project management Institute (PMI), Reviewer, Editorial Board Member of R&D Journals. Board Member & Program Director SF DAMA. SME in Data Governance, GDPR, HIPAA Compliance, Data Management, Data Architecture, Master Data, Data Quality, AI/ML, Analytics and reporting in Payment processing, Customer, Finance, CRM and License domains. Mentor in SFDC Mig, Data.com, ERP, Architecture, R&D, Embedded systems, VLSI, Adv Information processing. I am also reviewer for IEEE Silicon Valley Sr Member Elevation and Speaker, Volunteer for SFBay ACM. Reviewer of Journals in Springer Nature.
Joseph Jeganathan earned his Ph.D. in Psychiatric Nursing from Rajiv Gandhi University of Health Sciences, National Consortium for Ph.D. Nursing, (Indian Nursing Council), Bangalore, Karnataka, India in 2019. He completed his Postgraduate studies at the National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India 2011. He is working as an assistant professor in the Mental Health Nursing Department at the University of Bahrain, Kingdom of Bahrain. He has more than 16 years of Undergraduate and Postgraduate teaching experience. He has worked as a Professor at SGT University, Gurugram, Dept. of Nursing, IES University, Bhopal; Associate Professor Akal College of Nursing, Eternal University, Baru Sahib, India; and also worked as a teaching faculty in various nursing colleges in India and Nepal. Dr. Joseph Jeganathan has published many research articles in National and international Nursing Journals. His areas of research interest are Adolescent mental health, Life skills training, and Substance use disorders.
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Optimizing Patient Outcomes Through Multi-Source Data Analysis in Healthcare (Hardcover)
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Hardcover. Condition: new. Hardcover. The landscape of healthcare is transformed by the integration of advanced data analytics, especially in the realm of multi-source data analysis. By combining diverse datasets, such as electronic health records (EHRs), genetic information, wearable device data, and patient-reported outcomes, healthcare providers can gain a comprehensive understanding of a patient's health status. This approach creates more personalized treatment plans, enhances diagnostic accuracy, and supports early detection of potential health issues. Communication between various data sources allows for the identification of hidden trends and patterns, improving predictive capabilities and optimizing patient outcomes. As healthcare systems adopt this data-driven process, it is crucial to address challenges related to data privacy, integration, and the interpretation of complex datasets, ensuring the potential benefits of multi-source data analysis are realized in ethical and effective ways. Optimizing Patient Outcomes Through Multi-Source Data Analysis in Healthcare explores the transformative potential of big data and AI in healthcare, focusing on informed decision-making. It delves into the integration of vast, diverse datasets, analyzed through AI algorithms to enhance patient outcomes and operational efficiency. This book covers topics such as automation, machine learning, and neural networks, and is a useful resource for healthcare professionals, computer engineers, business owners, academicians, researchers, and data scientists. "This book explores the transformative potential of big data and AI in healthcare, focusing on informed decision-making. It delves into the integration of vast, diverse datasets such as EHRs, medical imaging, and genomic data, analyzed through AI algorithms to enhance patient outcomes and operational efficiency"-- Provided by publisher. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798369394205
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Optimizing Patient Outcomes Through Multi-Source Data Analysis in Healthcare (Hardcover)
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Hardcover. Condition: new. Hardcover. The landscape of healthcare is transformed by the integration of advanced data analytics, especially in the realm of multi-source data analysis. By combining diverse datasets, such as electronic health records (EHRs), genetic information, wearable device data, and patient-reported outcomes, healthcare providers can gain a comprehensive understanding of a patient's health status. This approach creates more personalized treatment plans, enhances diagnostic accuracy, and supports early detection of potential health issues. Communication between various data sources allows for the identification of hidden trends and patterns, improving predictive capabilities and optimizing patient outcomes. As healthcare systems adopt this data-driven process, it is crucial to address challenges related to data privacy, integration, and the interpretation of complex datasets, ensuring the potential benefits of multi-source data analysis are realized in ethical and effective ways. Optimizing Patient Outcomes Through Multi-Source Data Analysis in Healthcare explores the transformative potential of big data and AI in healthcare, focusing on informed decision-making. It delves into the integration of vast, diverse datasets, analyzed through AI algorithms to enhance patient outcomes and operational efficiency. This book covers topics such as automation, machine learning, and neural networks, and is a useful resource for healthcare professionals, computer engineers, business owners, academicians, researchers, and data scientists. "This book explores the transformative potential of big data and AI in healthcare, focusing on informed decision-making. It delves into the integration of vast, diverse datasets such as EHRs, medical imaging, and genomic data, analyzed through AI algorithms to enhance patient outcomes and operational efficiency"-- Provided by publisher. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798369394205
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Optimizing Patient Outcomes Through Multi-Source Data Analysis in Healthcare (Hardcover)
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Hardcover. Condition: new. Hardcover. The landscape of healthcare is transformed by the integration of advanced data analytics, especially in the realm of multi-source data analysis. By combining diverse datasets, such as electronic health records (EHRs), genetic information, wearable device data, and patient-reported outcomes, healthcare providers can gain a comprehensive understanding of a patient's health status. This approach creates more personalized treatment plans, enhances diagnostic accuracy, and supports early detection of potential health issues. Communication between various data sources allows for the identification of hidden trends and patterns, improving predictive capabilities and optimizing patient outcomes. As healthcare systems adopt this data-driven process, it is crucial to address challenges related to data privacy, integration, and the interpretation of complex datasets, ensuring the potential benefits of multi-source data analysis are realized in ethical and effective ways. Optimizing Patient Outcomes Through Multi-Source Data Analysis in Healthcare explores the transformative potential of big data and AI in healthcare, focusing on informed decision-making. It delves into the integration of vast, diverse datasets, analyzed through AI algorithms to enhance patient outcomes and operational efficiency. This book covers topics such as automation, machine learning, and neural networks, and is a useful resource for healthcare professionals, computer engineers, business owners, academicians, researchers, and data scientists. "This book explores the transformative potential of big data and AI in healthcare, focusing on informed decision-making. It delves into the integration of vast, diverse datasets such as EHRs, medical imaging, and genomic data, analyzed through AI algorithms to enhance patient outcomes and operational efficiency"-- Provided by publisher. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9798369394205
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