Predicting Pregnancy Complications Through Artificial Intelligence And Machine Learning

The lack of comprehensive, innovative insights into the intricate world of pregnancy complication prediction is a pressing concern, as these complications can severely impact the health and wellbeing of pregnant patients. As the complexities of maternal healthcare continue to evolve, scholars grapple with the challenge of staying at the forefront of research and innovation in this critical field. The unpredictability of pregnancy complications poses significant risks to positive patient outcomes, demanding novel approaches to diagnosis and prevention. The academic community seeks a solution that can bridge the gap between traditional research and the transformative potential of technological advancements in healthcare. Technological Tools for Predicting Pregnancy Complications not only identify the problem but offer an authoritative solution. It serves as a beacon of knowledge for academic scholars, providing a holistic exploration of how Artificial Intelligence (AI) and Machine Learning (ML) technologies can revolutionize maternal healthcare. With a laser focus on predictive models, comprehensive health data analysis, and innovative algorithmic approaches, this book equips scholars with the tools they need to navigate the ever-evolving landscape of pregnancy complications. Academic scholars will find a treasure trove of insights, spanning from the fundamentals of AI and ML in healthcare to the application of IoT devices and wearable sensors for expectant mothers.

Artificial intelligence models are being used to make labor and delivery safer for mothers and newborns. Sensors are exploited to gauge health parameters, and machine learning techniques are investigated to predict the health conditions of patients to assist medical practitioners. This is a critical area of study as maternal and infant health are indispensable for a healthy society. Predicting Pregnancy Complications Through Artificial Intelligence and Machine Learning considers the recent advances, challenges, and best practices of artificial intelligence and machine learning in relation to pregnancy complications. Covering key topics such as pregnancy complications, wearable sensors, and healthcare technologies, this premier reference source is ideal for nurses, doctors, computer scientists, medical professionals, industry professionals, researchers, academicians, scholars, instructors, and students.

This book aims to provide a refreshing, friendly and exciting manner artificial intelligence (AI) theoretical concepts and practical methods applied in obstetrics and gynaecology. The book follows the nine and a half months journey from preconception till birth alongside with AI. It discusses topics such as the poignant role of AI in improving the change of women getting pregnant; AI methods for detecting congenital anomalies in first and second trimester foetal sonography; how AI aids physicians in determining what type of birth should be deployed (vaginal versus caesarean); how AI can predict pre-eclampsia, preterm birth, mortality, birth weight, miscarriage, postpartum depression, etc. Additionally, it provides information on AI used for perinatal depression, for the evaluation of the relationship between pollutants and pregnancy outcome and even how AI tools can improve physician training in labour and delivery. The book is designed for bioinformaticians, obstetric and gynaecology researchers and physicians, and all those who wish to learn how to explore, analyse, find novel potential solutions for the challenging domains: obstetrics and gynaecology. Likewise, this book will be useful for application engineers who wish to use AI paradigms in areas such as engineering and science too.

In the world of academia, scholars and researchers are confronted with a rapidly expanding knowledge base in Artificial Intelligence (AI) and nanotechnology. The integration of these two groundbreaking fields presents an intricate web of concepts, innovations, and interdisciplinary applications that can overwhelm even the most astute academic minds. Staying up to date with the latest developments and effectively navigating this complex terrain has become a pressing challenge for those striving to contribute meaningfully to these fields. Artificial Intelligence in the Age of Nanotechnology is a transformative solution meticulously crafted to address the academic community's knowledge gaps and challenges. This comprehensive book serves as the guiding light for scholars, researchers, and students grappling with the dynamic synergy between AI and Nanotechnology. It offers a structured and authoritative exploration of the core principles and transformative applications of these domains across diverse fields. By providing clarity and depth, it empowers academics to stay at the forefront of innovation and make informed contributions.

In the dynamic world of manufacturing, the industry has grappled with ongoing issues such as expensive machine maintenance, operational inefficiencies, and the production of defective products. The need for informed decision-making to maintain quality, meet deadlines, and prevent disruptions is more crucial than ever. Enter Using Real-Time Data and AI for Thrust Manufacturing, a groundbreaking book that addresses these challenges head-on. As Industry 4.0 transforms the manufacturing sector through the integration of the Internet of Things (IoT) and artificial intelligence (AI), this book serves as a beacon for academic scholars and industry professionals alike, offering profound insights into the world of AI-driven industry solutions. The objective of this book is clear—to revolutionize the manufacturing sector by leveraging human expertise and AI-driven data technologies. By delving into the realms of Industry 4.0, IoT, and AI, the book systematically tackles issues such as costly downtime, inefficient processes, and the production of substandard products. With a focus on turning raw data into meaningful insights, the book explores AI applications like machine learning and deep learning, natural language processing, and machine vision. From predictive maintenance to improved demand forecasting, quality assurance, inspection, and warehouse automation, the book positions AI as the linchpin of "Industry 4.0," ensuring not only cost savings but also safety improvements and supply-chain efficiencies.

The main focus of this book is the examination of women’s health issues and the role machine learning can play as a solution to these challenges. This book will illustrate advanced, innovative techniques/frameworks/concepts/machine learning methodologies, enhancing the future healthcare system. Combating Women’s Health Issues with Machine Learning: Challenges and Solutions examines the fundamental concepts and analysis of machine learning algorithms. The editors and authors of this book examine new approaches for different age-related medical issues that women face. Topics range from diagnosing diseases such as breast and ovarian cancer to using deep learning in prenatal ultrasound diagnosis. The authors also examine the best machine learning classifier for constructing the most accurate predictive model for women’s infertility risk. Among the topics discussed are gender differences in type 2 diabetes care and its management as it relates to gender using artificial intelligence. The book also discusses advanced techniques for evaluating and managing cardiovascular disease symptoms, which are more common in women but often overlooked or misdiagnosed by many healthcare providers. The book concludes by presenting future considerations and challenges in the field of women’s health using artificial intelligence. This book is intended for medical researchers, healthcare technicians, scientists, programmers and graduate-level students looking to understand better and develop applications of machine learning/deep learning in healthcare scenarios, especially concerning women’s health conditions.

The Handbook of Research on AI and ML for Intelligent Machines and Systems offers a comprehensive exploration of the pivotal role played by artificial intelligence (AI) and machine learning (ML) technologies in the development of intelligent machines. As the demand for intelligent machines continues to rise across various sectors, understanding the integration of these advanced technologies becomes paramount. While AI and ML have individually showcased their capabilities in developing robust intelligent machine systems and services, their fusion holds the key to propelling intelligent machines to a new realm of transformation. By compiling recent advancements in intelligent machines that rely on machine learning and deep learning technologies, this book serves as a vital resource for researchers, graduate students, PhD scholars, faculty members, scientists, and software developers. It offers valuable insights into the key concepts of AI and ML, covering essential security aspects, current trends, and often overlooked perspectives that are crucial for achieving comprehensive understanding. It not only explores the theoretical foundations of AI and ML but also provides guidance on applying these techniques to solve real-world problems. Unlike traditional texts, it offers flexibility through its distinctive module-based structure, allowing readers to follow their own learning paths.