Data Mining and Medical Knowledge Management: Cases and Applications


Book Description

The healthcare industry produces a constant flow of data, creating a need for deep analysis of databases through data mining tools and techniques resulting in expanded medical research, diagnosis, and treatment. Data Mining and Medical Knowledge Management: Cases and Applications presents case studies on applications of various modern data mining methods in several important areas of medicine, covering classical data mining methods, elaborated approaches related to mining in electroencephalogram and electrocardiogram data, and methods related to mining in genetic data. A premier resource for those involved in data mining and medical knowledge management, this book tackles ethical issues related to cost-sensitive learning in medicine and produces theoretical contributions concerning general problems of data, information, knowledge, and ontologies.




Medical Informatics


Book Description

Comprehensively presents the foundations and leading application research in medical informatics/biomedicine. The concepts and techniques are illustrated with detailed case studies. Authors are widely recognized professors and researchers in Schools of Medicine and Information Systems from the University of Arizona, University of Washington, Columbia University, and Oregon Health & Science University. Related Springer title, Shortliffe: Medical Informatics, has sold over 8000 copies The title will be positioned at the upper division and graduate level Medical Informatics course and a reference work for practitioners in the field.




Knowledge Management


Book Description

Provides comprehensive, in-depth coverage of all issues related to knowledge management, including conceptual, methodological, technical, and managerial issues. Presents the opportunities, future challenges, and emerging trends related to this subject.




Process Mining in Healthcare


Book Description

What are the possibilities for process mining in hospitals? In this book the authors provide an answer to this question by presenting a healthcare reference model that outlines all the different classes of data that are potentially available for process mining in healthcare and the relationships between them. Subsequently, based on this reference model, they explain the application opportunities for process mining in this domain and discuss the various kinds of analyses that can be performed. They focus on organizational healthcare processes rather than medical treatment processes. The combination of event data and process mining techniques allows them to analyze the operational processes within a hospital based on facts, thus providing a solid basis for managing and improving processes within hospitals. To this end, they also explicitly elaborate on data quality issues that are relevant for the data aspects of the healthcare reference model. This book mainly targets advanced professionals involved in areas related to business process management, business intelligence, data mining, and business process redesign for healthcare systems as well as graduate students specializing in healthcare information systems and process analysis.




Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications


Book Description

Advancements in data science have created opportunities to sort, manage, and analyze large amounts of data more effectively and efficiently. Applying these new technologies to the healthcare industry, which has vast quantities of patient and medical data and is increasingly becoming more data-reliant, is crucial for refining medical practices and patient care. Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications is a vital reference source that examines practical applications of healthcare analytics for improved patient care, resource allocation, and medical performance, as well as for diagnosing, predicting, and identifying at-risk populations. Highlighting a range of topics such as data security and privacy, health informatics, and predictive analytics, this multi-volume book is ideally designed for doctors, hospital administrators, nurses, medical professionals, IT specialists, computer engineers, information technologists, biomedical engineers, data-processing specialists, healthcare practitioners, academicians, and researchers interested in current research on the connections between data analytics in the field of medicine.




Classification and Clustering in Biomedical Signal Processing


Book Description

Advanced techniques in image processing have led to many innovations supporting the medical field, especially in the area of disease diagnosis. Biomedical imaging is an essential part of early disease detection and often considered a first step in the proper management of medical pathological conditions. Classification and Clustering in Biomedical Signal Processing focuses on existing and proposed methods for medical imaging, signal processing, and analysis for the purposes of diagnosing and monitoring patient conditions. Featuring the most recent empirical research findings in the areas of signal processing for biomedical applications with an emphasis on classification and clustering techniques, this essential publication is designed for use by medical professionals, IT developers, and advanced-level graduate students.




Intelligent Computing: An Introduction to Artificial Intelligence Book


Book Description

In this book named ‘Intelligent Computing: An Introduction to Artificial Intelligence.’ the authors try to give detailed information on various aspects of Intelligent computing. This book consists of seven chapters from Introduction to AI to the Future of AI. The first chapter consists of the Introduction, history importance, and impact of intelligent computing in various fields. The Second chapter gives information about the Foundations of Artificial Intelligence which is cognitive science and its relation to AI. It also explains the Key concepts of Machine learning, Neural networks, Natural language processing and followed by concepts of Robotics. The third chapter explains Intelligent Computing Techniques named Supervised learning: Linear regression, Logistic regression, Support vector machines, Unsupervised learning: Clustering algorithms, Dimensionality reduction, Association rule mining, Deep learning: Neural network architectures, Convolutional neural networks, Recurrent neural networks: Generative adversarial networks, Reinforcement learning, Markov decision processes, Q-learning, Deep reinforcement learning. The fourth chapter consists of information about Applications of Intelligent Computing. Natural language processing applications: Sentiment analysis, Speech recognition, Machine translation, Computer vision applications like Object detection and recognition, Image classification, Facial recognition, Robotics applications Like Autonomous Vehicles, Industrial Automation, human robots, Healthcare applications, Disease diagnosis, Medical Image Analysis & Drug discovery. The fifth chapter consists of topics on the Ethical and Social prospective of the Implications of Intelligent Computing covers the Limitations & strengths of AI algorithms, Privacy and security concerns, Automation and its impact on job displacement also about governance and regulations on AI by the government. The sixth Chapter contains Future Directions and Challenges in Intelligent Computing Advances like interpretability of AI systems, Human-AI collaboration and augmentation, and Addressing ethical and societal challenges. The last chapter gives a conclusion about the topic: key points of AI, its Potential impact in the future & required Encouragement for further exploration of AI and intelligent computing. This book gives detailed enough information for the reader to enhance their knowledge of Intelligent Computing and AI.




Data Mining: Concepts, Methodologies, Tools, and Applications


Book Description

Data mining continues to be an emerging interdisciplinary field that offers the ability to extract information from an existing data set and translate that knowledge for end-users into an understandable way. Data Mining: Concepts, Methodologies, Tools, and Applications is a comprehensive collection of research on the latest advancements and developments of data mining and how it fits into the current technological world.




Advances in Visual Computing


Book Description

It is with great pleasure that we present the proceedings of the 6th Inter- tional, Symposium on Visual Computing (ISVC 2010), which was held in Las Vegas, Nevada. ISVC provides a common umbrella for the four main areas of visual computing including vision, graphics, visualization, and virtual reality. The goal is to provide a forum for researchers, scientists, engineers, and pr- titioners throughout the world to present their latest research ?ndings, ideas, developments, and applications in the broader area of visual computing. This year, the program consisted of 14 oral sessions, one poster session, 7 special tracks, and 6 keynote presentations. The response to the call for papers was very good; we received over 300 submissions for the main symposium from which we accepted 93 papers for oral presentation and 73 papers for poster p- sentation. Special track papers were solicited separately through the Organizing and Program Committees of each track. A total of 44 papers were accepted for oral presentation and 6 papers for poster presentation in the special tracks.




Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects


Book Description

ICDM / MLDM Medaillie (limited edition) Meissner Porcellan, the “White Gold” of King August the Strongest of Saxonia ICDM 2008 was the eighth event of the Industrial Conference on Data Mining held in Leipzig (www.data-mining-forum.de). For this edition the Program Committee received 116 submissions from 20 countries. After the peer-review process, we accepted 36 high-quality papers for oral presentation, which are included in these proceedings. The topics range from aspects of classification and prediction, clustering, Web mining, data mining in medicine, applications of data mining, time series and frequent pattern mining, and association rule mining. Thirteen papers were selected for poster presentations that are published in the ICDM Poster Proceeding Volume. In conjunction with ICDM there were three workshops focusing on special hot application-oriented topics in data mining. The workshop Data Mining in Life Science DMLS 2008 was held the third time this year and the workshop Data Mining in Marketing DMM 2008 ran for the second time this year. Additionally, we introduced an International Workshop on Case-Based Reasoning for Multimedia Data CBR-MD.