Expert System Techniques In Biomedical Science Practice

Before the integration of expert systems in biomedical science, complex problems required human expertise to solve them through conventional procedural methods. Advancements in expert systems allow for knowledge to be extracted when no human expertise is available and increases productivity through quick diagnosis. Expert System Techniques in Biomedical Science Practice is an essential scholarly resource that contains innovative research on the methods by which an expert system is designed to solve complex problems through the automation of decision making through the use of if-then-else rules rather than conventional procedural methods. Featuring coverage on a broad range of topics such as image processing, bio-signals, and cognitive AI, this book is a vital reference source for computer engineers, information technologists, biomedical engineers, data-processing specialists, medical professionals, and industrialists within the fields of biomedical engineering, pervasive computing, and natural language processing.

Before the integration of expert systems in biomedical science, complex problems required human expertise to solve them through conventional procedural methods. Advancements in expert systems allow for knowledge to be extracted when no human expertise is available and increases productivity through quick diagnosis. Expert System Techniques in Biomedical Science Practice is an essential scholarly resource that contains innovative research on the methods by which an expert system is designed to solve complex problems through the automation of decision making through the use of if-then-else rules rather than conventional procedural methods. Featuring coverage on a broad range of topics such as image processing, bio-signals, and cognitive AI, this book is a vital reference source for computer engineers, information technologists, biomedical engineers, data-processing specialists, medical professionals, and industrialists within the fields of biomedical engineering, pervasive computing, and natural language processing.

With the technology innovations dentistry has witnessed in all its branches over the past three decades, the need for more precise diagnostic tools and advanced imaging methods has become mandatory across the industry. Recent advancements to imaging systems are playing an important role in efficient diagnoses, treatments, and surgeries. Computational Techniques for Dental Image Analysis provides innovative insights into computerized methods for automated analysis. The research presented within this publication explores pattern recognition, oral pathologies, and diagnostic processing. It is designed for dentists, professionals, medical educators, medical imaging technicians, researchers, oral surgeons, and students, and covers topics centered on easier assessment of complex cranio-facial tissues and the accurate diagnosis of various lesions at early stages.

The field of healthcare is seeing a rapid expansion of technological advancement within current medical practices. The implementation of technologies including neural networks, multi-model imaging, genetic algorithms, and soft computing are assisting in predicting and identifying diseases, diagnosing cancer, and the examination of cells. Implementing these biomedical technologies remains a challenge for hospitals worldwide, creating a need for research on the specific applications of these computational techniques. Deep Neural Networks for Multimodal Imaging and Biomedical Applications provides research exploring the theoretical and practical aspects of emerging data computing methods and imaging techniques within healthcare and biomedicine. The publication provides a complete set of information in a single module starting from developing deep neural networks to predicting disease by employing multi-modal imaging. Featuring coverage on a broad range of topics such as prediction models, edge computing, and quantitative measurements, this book is ideally designed for researchers, academicians, physicians, IT consultants, medical software developers, practitioners, policymakers, scholars, and students seeking current research on biomedical advancements and developing computational methods in healthcare.

The aim of Mechano-Electric Correlations in the Human Physiological System is to present the mechanical and electrical properties of human soft tissues and the mathematical models related to the evaluation of these properties in time, as well as their biomedical applications. This book also provides an overview of the bioelectric signals of soft tissues from various parts of the human body. In addition, this book presents the basic dielectric and viscoelastic characteristics of soft tissues, an introduction to the measurement and characteristics of bioelectric signals and their relationship with the mechanical activity, electromyography and the correlation of electromyograms with the muscle activity in normal and certain clinical conditions. The authors also present a case study on the effect of lymphatic filariasis on the mechanical and electrical activity of the muscle. Features: Explains the basics of electrical and mechanical properties of soft tissues in time and frequency domain along with the mathematical models of soft tissue mechanics Explores the correlation of electrical properties with the mechanical properties of biological soft tissues using computational techniques Provides a detailed introduction to electrophysiological signals along with the types, applications, properties, problems and associated mathematical models Explains the electromechanics of muscles using electromyography recordings from various muscles of the human physiological system Presents a case study on the effect of lymphatic filariasis on the mechanical and electrical activity of the muscle Mechano-Electric Correlations in the Human Physiological System is intended for biomedical engineers, researchers and medical scientists as well graduate and undergraduate students working on the mechanical properties of soft tissues.

The results of computational model simulations allow researchers and clinicians to make predictions about what will happen in the biological systems that are being studied in response to changing conditions for a disease or disorder. With a well-developed computational model, researchers and clinicians can better understand the cause of a disease or a disorder and predict treatment results. Computational Models for Biomedical Reasoning and Problem Solving is a critical scholarly publication that provides insightful strategies to developing computational models that allow for the better understanding and treatment of various diseases and disorders. Featuring topics such as biomedicine, neuroscience, and artificial intelligence, this book is ideal for practitioners, clinicians, researchers, psychologists, and engineers.

For as much as we know about DNA and gene expression, many more mysteries remain to be solved. Epigenetics and epigenomics seek to study heritable modifications in gene expression that do not involve underlying DNA sequences to further human health changes. Examining the Causal Relationship Between Genes, Epigenetics, and Human Health provides innovative research methods and applications of chemical activation or deactivation of genes without altering the original DNA sequence. While highlighting topics including gene expression, personalized medicine, and public policy, this book is ideal for researchers, geneticists, biologists, medical professionals, students, and academics seeking current research on the expanding fields of genomics, epigenomics, proteomics, pharmacogenomics, and genome-wide association studies.

The rapid development of new technologies has created a lasting impact in the healthcare sector during the past decades. Due to this influence, potential clinical problems have decreased while the quality of healthcare delivery and overall user friendliness has increased and contributed to cost-effective healthcare systems. Biomedical and Clinical Engineering for Healthcare Advancement is an essential reference source that discusses growth in healthcare applications driven by the adoption of new technologies, as well as the expansion of machine learning algorithms for clinical decision making. It focuses on combining vision, motion, data acquisition, and automated control to accelerate the development of affordable and portable medical devices. Featuring research on topics such as artificial intelligence, drug delivery, and retinal imaging, this book is ideally designed for healthcare professionals, biomedical engineers, biomedical professionals, clinicians, hospital directors, physicians, medical students, and clinical researchers.