Authored by a leading voice in the field, Machine Learning for Healthcare provides a gateway to revolutionize the understanding of medicine and patient care. The book unlocks the secrets of clinical data, harnessing the power of machine learning to diagnose diseases with unprecedented accuracy, and predicting patient outcomes with confidence. From the intricacies of disease progression to the human factors shaping healthcare delivery, each chapter is a testament to the transformative potential of AI in medicine. Readers include anyone passionate about the intersection of technology and human well-being from healthcare professionals eager to stay ahead of the curve, to bystanders fascinated by the possibilities of AI.

This reference text presents the usage of artificial intelligence in healthcare and discusses the challenges and solutions of using advanced techniques like wearable technologies and image processing in the sector. Features: Focuses on the use of artificial intelligence (AI) in healthcare with issues, applications, and prospects Presents the application of artificial intelligence in medical imaging, fractionalization of early lung tumour detection using a low intricacy approach, etc Discusses an artificial intelligence perspective on wearable technology Analyses cardiac dynamics and assessment of arrhythmia by classifying heartbeat using electrocardiogram (ECG) Elaborates machine learning models for early diagnosis of depressive mental affliction This book serves as a reference for students and researchers analyzing healthcare data. It can also be used by graduate and post graduate students as an elective course.

This book discusses the advancements in artificial intelligent techniques used in the well-being of human healthcare. It details the techniques used in collection, storage and analysis of data and their usage in different healthcare solutions. It also discusses the techniques of predictive analysis in early diagnosis of critical diseases. The edited book is divided into four parts – part A discusses introduction to artificial intelligence and machine learning in healthcare; part B highlights different analytical techniques used in healthcare; part C provides various security and privacy mechanisms used in healthcare; and finally, part D exemplifies different tools used in visualization and data analytics.

This book presents information about the use of technology to support the prevention and management of drug safety issues: pharmacovigilance (PV), medication errors, drug-related problems (DRPs), counterfeit medicines and other drug safety issues. Adapting new technologies/information technologies, mobile technologies and social media has contributed effectively to safety practices for medications, with this book providing comprehensive information as a guide to its challenges and potential. Technology for Drug Safety provides practicing and trainee pharmacists, pharmacy technicians, pharmacy educators, researchers, public health policy makers, healthcare professionals and medical educators with vital information about the impact of technology on drug safety-related issues. It describes the current status of the practice, the challenges in the field and recommendations for the effective use of technology in drug safety practice including clinical trials and drug development, PV, detection of adverse drug reactions, reporting and management, medication errors detection, reporting and management, DRPs, counterfeit and substandard medications, and other safety issues.

Artificial Intelligence (AI) in general and machine learning (ML) and deep learning (DL) in particular and related digital technologies are a couple of fledging paradigms that next-generation healthcare services are sprinting towards. These digital technologies can transform various aspects of healthcare, leveraging advances in computing and communication power. With a new spectrum of business opportunities, AI-powered healthcare services will improve the lives of patients, their families, and societies. However, the application of AI in the healthcare field requires special attention given the direct implication with human life and well-being. Rapid progress in AI leads to the possibility of exploiting healthcare data for designing practical tools for automated diagnosis of chronic diseases such as dementia and diabetes. This book highlights the current research trends in applying AI models in various disease diagnoses and prognoses to provide enhanced healthcare solutions. The primary audience of the book are postgraduate students and researchers in the broad domain of healthcare technologies. Features In-depth coverage of the role of AI in smart healthcare Research guidelines for AI and data science researchers/practitioners interested in the healthcare sector Comprehensive coverage on security and privacy issues for AI in smart healthcare

The 1st International Conference on Recent Advances in Computing Sciences (RACS-2022) organized by the School of Computer Application, Lovely Professional University, Jalandhar, Punjab from 4th to 5th November, 2022. The conference focuses on discussing issues, exchanging ideas, and the most recent innovations towards the advancement of research in the field of Computing Sciences and Technology. All technical sessions were predominantly related to Data Science, Artificial Intelligence, Remote Sensing, Image Processing, Computer Vision, Data Forensics, Cyber Security, Computational Sciences, Simulation & Modelling, Business Analytics, and Machine Learning. The main objective of this conference is to provide a common platform for academia and industry to discuss various technological challenges and share cognitive thoughts. It provided a thought-provoking platform to discuss and disseminate novel solutions for real-world problems in a dynamic and changing technological environment. The main success of RACS-2022 is to give an opportunity for the participants to enhance their knowledge of recent computing technologies.

Security implementation is crucial in the Internet of Medical Things (IoMT) as it ensures the protection of sensitive medical data and prevents unauthorized access to or manipulation of devices and systems. This book covers different aspects of security implementations and challenges in IoMT and aims to bring researchers together to contribute their findings to recommend new methodologies and feasible solutions for implementing security and novel architectures in artificial intelligence, machine learning, and data science in the field of healthcare and IoT. IoMT includes a wide range of connected medical devices and systems, such as wearable devices, medical sensors, and electronic health records, that collect, store, and share sensitive medical information. Without proper security measures, this information could be compromised, leading to serious privacy breaches, financial fraud, and even physical harm to patients.

The book is a comprehensive guide that explores the use of artificial intelligence and machine learning in drug discovery and development covering a range of topics, including the use of molecular modeling, docking, identifying targets, selecting compounds, and optimizing drugs. The intersection of Artificial Intelligence (AI) and Machine Learning (ML) within the field of drug design and development represents a pivotal moment in the history of healthcare and pharmaceuticals. The remarkable synergy between cutting-edge technology and the life sciences has ushered in a new era of possibilities, offering unprecedented opportunities, formidable challenges, and a tantalizing glimpse into the future of medicine. AI can be applied to all the key areas of the pharmaceutical industry, such as drug discovery and development, drug repurposing, and improving productivity within a short period. Contemporary methods have shown promising results in facilitating the discovery of drugs to target different diseases. Moreover, AI helps in predicting the efficacy and safety of molecules and gives researchers a much broader chemical pallet for the selection of the best molecules for drug testing and delivery. In this context, drug repurposing is another important topic where AI can have a substantial impact. With the vast amount of clinical and pharmaceutical data available to date, AI algorithms find suitable drugs that can be repurposed for alternative use in medicine. This book is a comprehensive exploration of this dynamic and rapidly evolving field. In an era where precision and efficiency are paramount in drug discovery, AI and ML have emerged as transformative tools, reshaping the way we identify, design, and develop pharmaceuticals. This book is a testament to the profound impact these technologies have had and will continue to have on the pharmaceutical industry, healthcare, and ultimately, patient well-being. The editors of this volume have assembled a distinguished group of experts, researchers, and thought leaders from both the AI, ML, and pharmaceutical domains. Their collective knowledge and insights illuminate the multifaceted landscape of AI and ML in drug design and development, offering a roadmap for navigating its complexities and harnessing its potential. In each section, readers will find a rich tapestry of knowledge, case studies, and expert opinions, providing a 360-degree view of AI and ML’s role in drug design and development. Whether you are a researcher, scientist, industry professional, policymaker, or simply curious about the future of medicine, this book offers 19 state-of-the-art chapters providing valuable insights and a compass to navigate the exciting journey ahead. Audience The book is a valuable resource for a wide range of professionals in the pharmaceutical and allied industries including researchers, scientists, engineers, and laboratory workers in the field of drug discovery and development, who want to learn about the latest techniques in machine learning and AI, as well as information technology professionals who are interested in the application of machine learning and artificial intelligence in drug development.

Offering a holistic view of the pioneering trends and innovations in smart healthcare management, this book focuses on the methodologies, frameworks, design issues, tools, architectures, and technologies necessary to develop and understand intelligent healthcare systems and emerging applications in the present era. Smart Technologies in Healthcare Management: Pioneering Trends and Applications provides an overview of various technical and innovative aspects, challenges, and issues in smart healthcare, along with recent and novel findings. It highlights the latest advancements and applications in the field of intelligent systems and explores the importance of cloud computing and the design of sensors in an IoT system. The book offers algorithms and a framework with models in machine learning and AI for smart healthcare management. A detailed flow chart and innovative and modified methodologies related to intelligent computing in healthcare are discussed, as well as real-world-based examples so that readers can compare technical concepts with daily life concepts. This book will be a useful reference for academicians and the healthcare industry, along with professionals interested in exploring innovations in varied applicational areas of AI, IoT, and machine learning. Researchers, startup companies, and entrepreneurs will also find this book of interest.