Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems

The book explores the application of computational intelligence in healthcare, covering areas such as diagnostic systems, machine learning, and deep learning. It provides guidance on developing intelligent systems and offers research findings and solutions to healthcare challenges.

Format: Hardback
Length: 379 pages
Publication date: 19 May 2022
Publisher: Taylor & Francis Ltd

Computational intelligence (CI) has emerged as a powerful tool in the field of biomedical and healthcare, offering a wide range of applications that have the potential to revolutionize the way we approach healthcare problems. In this article, we will explore different dimensions of CI applications and illustrate their use in the solution of assorted real-world biomedical and healthcare problems.

One of the key areas of CI application in healthcare is the development of intelligence-based diagnostic systems. These systems use machine learning and deep learning algorithms to analyze medical data, such as images, scans, and patient records, and identify patterns that can indicate the presence of diseases or other health conditions. By leveraging the power of AI, these systems can provide accurate and timely diagnoses, which can lead to better patient outcomes and reduced healthcare costs.

Another area of CI application in healthcare is the development of efficient models and cost-effective machines. These machines can be used for a variety of tasks, such as drug discovery, medical imaging, and patient monitoring. By using advanced algorithms and machine learning techniques, these machines can automate repetitive tasks and improve efficiency, which can lead to cost savings for healthcare providers and improved patient care.

In addition to these applications, CI has also been used to address a range of concerning issues in healthcare systems. For example, researchers have used CI to develop algorithms that can predict the spread of pandemics and mitigate their risk. By analyzing data on social interactions, travel patterns, and other factors, these algorithms can identify areas that are at high risk of infection and provide early warning signals to healthcare providers.

Furthermore, CI has been used to improve the interoperability of healthcare applications and protect the privacy and security of patient data. Protocol design, prototyping, experimental evaluation, real test beds, and empirical characterization of security and privacy interoperability issues in healthcare applications have all been explored and illustrated using CI techniques.

As CI continues to evolve and advance, it is clear that it will have a significant impact on the future of healthcare. By leveraging the power of AI and machine learning, healthcare providers can improve patient care, reduce costs, and address a range of pressing healthcare problems. However, there are also challenges that need to be addressed, such as the ethical implications of AI and the need for robust data security and privacy measures.

In conclusion, computational intelligence has the potential to transform the field of biomedical and healthcare by offering a wide range of applications that can improve patient care, reduce costs, and address a range of pressing healthcare problems. While there are challenges that need to be addressed, the future of CI in healthcare looks bright, and we can expect to see continued innovation and progress in this area in the years to come.

Dimension: 234 x 156 (mm)
ISBN-13: 9781032036724