Nlp Natural Language Processing In Bioinformatics Healthcare Applications
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NLP (NATURAL LANGUAGE PROCESSING) IN BIOINFORMATICS HEALTHCARE APPLICATIONS
Author: Dr. Omar Isam Al Mrayat
language: en
Publisher: Xoffencer International Book Publication House
Release Date: 2024-12-31
It is possible for healthcare systems to link all of the individuals engaged in healthcare and enhance the quality of treatment they deliver with the assistance of emerging technologies such as blockchain, artificial intelligence, big data, cloud/edge computing, and the internet of things (IoT). There are three primary groups that comprise smart healthcare: the general public, healthcare providers, and other parties participating in the healthcare sector. Smart healthcare is comprised of these three categories. There are many instances of representative smart healthcare scenarios that are important to the participants. Some examples include smart homes, hospitals, healthcare administration, public health, rehabilitation therapy, intelligent life science research and development, and so on. Natural language processing (NLP) is a subfield of artificial intelligence and computer science that emphasises on the automated representation, analysis, and understanding of human language. There has been a meteoric rise in the popularity of natural language processing (NLP) over the last several years, which has piqued the attention of a number of academic organizations. Natural language processing (NLP) is essential to the delivery of intelligent healthcare since human language serves as a universal data input technique for intelligent medical systems. Understanding human language and communicating with people is made possible by natural language processing (NLP). Speaking and writing are both essential components of natural language; the former includes items like dictionaries, essays
Artificial Intelligence in Healthcare
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
Natural Language Processing and Text Mining
Author: Anne Kao
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-03-06
The topic this book addresses originated from a panel discussion at the 2004 ACM SIGKDD (Special Interest Group on Knowledge Discovery and Data Mining) Conference held in Seattle, Washington, USA. We the editors or- nized the panel to promote discussion on how text mining and natural l- guageprocessing,tworelatedtopicsoriginatingfromverydi?erentdisciplines, can best interact with each other, and bene?t from each other’s strengths. It attracted a great deal of interest and was attended by 200 people from all over the world. We then guest-edited a special issue of ACM SIGKDD Exp- rations on the same topic, with a number of very interesting papers. At the same time, Springer believed this to be a topic of wide interest and expressed an interest in seeing a book published. After a year of work, we have put - gether 11 papers from international researchers on a range of techniques and applications. We hope this book includes papers readers do not normally ?nd in c- ference proceedings, which tend to focus more on theoretical or algorithmic breakthroughs but are often only tried on standard test data. We would like to provide readers with a wider range of applications, give some examples of the practical application of algorithms on real-world problems, as well as share a number of useful techniques.