Machine Learning for Critical Internet of Medical Things: Applications and Use Cases

This book explores the applications, challenges, and future trends of machine learning in the medical domain, covering administrative processes, infectious diseases, personalized treatments, disease diagnosis, patient identification, drug modeling, and innovations. It highlights how machine learning enhances physicians' capabilities and improves the healthcare system's efficiency.

Format: Paperback / softback
Length: 261 pages
Publication date: 04 February 2023
Publisher: Springer Nature Switzerland AG

This comprehensive book delves into the realm of machine learning, exploring its applications, challenges, and future trends in the medical domain. It encompasses both fundamental and advanced topics, shedding light on how machine learning can revolutionize various aspects of healthcare.

One of the key advantages of machine learning in the medical field is its ability to streamline administrative processes within hospitals. By leveraging machine learning algorithms, hospitals can automate tasks such as appointment scheduling, medication management, and patient data entry, leading to increased efficiency and reduced errors. This not only improves patient care but also frees up healthcare professionals to focus on more critical aspects of their work.

In the realm of infectious diseases, machine learning plays a pivotal role in identifying and treating patients. By analyzing large datasets of medical records, machine learning algorithms can detect patterns and similarities between patients, enabling healthcare professionals to make accurate diagnoses and develop personalized treatment plans. This approach has proven particularly effective in combating infectious diseases such as COVID-19, where rapid identification and treatment are crucial.

Personalized medical treatments are another area where machine learning shines. By analyzing a patient's genetic information, medical history, and other relevant data, machine learning algorithms can generate personalized treatment plans that are tailored to the individual's specific needs. This approach has the potential to improve patient outcomes and reduce the risk of side effects associated with conventional treatments.

Machine learning also plays a significant role in disease diagnosis. By analyzing medical images, such as X-rays, CT scans, and MRI scans, machine learning algorithms can identify patterns and abnormalities that may indicate the presence of a disease. This can lead to faster and more accurate diagnoses, which can save lives and reduce the burden of healthcare costs.

Furthermore, machine learning can be used to develop new types of therapies and treatments. By analyzing patient data and clinical trials, machine learning algorithms can identify potential drug targets and develop new drugs that are more effective and have fewer side effects. This has the potential to revolutionize the pharmaceutical sector and improve patient care.

In addition to its applications in healthcare, machine learning can also be leveraged to enhance physician and doctor medical capabilities. By analyzing patient data and medical records, machine learning algorithms can provide physicians with insights and recommendations that can help them make more informed decisions about patient care. This can lead to better patient outcomes and improved patient satisfaction.

The book also illustrates advanced, innovative techniques, frameworks, concepts, and methodologies of machine learning that will enhance the efficiency and effectiveness of the healthcare system. These techniques include deep learning, natural language processing, and transfer learning, which have the potential to revolutionize the way we diagnose and treat medical conditions.

Overall, this comprehensive book provides researchers in machine and deep learning with a conceptual understanding of various methodologies of implementing the technologies in medical areas. It discusses the role machine learning and IoT play into locating different virus and diseases across the globe, such as COVID-19, Ebola, and cervical cancer. It also includes fundamentals and advances in machine learning in the medical field, supported by significant case studies and practical applications. By leveraging the power of machine learning, the healthcare system can become more efficient, effective, and patient-centered, leading to improved health outcomes for all.

Weight: 421g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030809300
Edition number: 1st ed. 2022