Applied Machine Learning For Healthcare And Life Sciences Using Aws
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Applied Machine Learning For Healthcare And Life Sciences Using Aws
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Author : Ujjwal Ratan
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-11-25
Applied Machine Learning For Healthcare And Life Sciences Using Aws written by Ujjwal Ratan and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-25 with Computers categories.
Build real-world artificial intelligence apps on AWS to overcome challenges faced by healthcare providers and payers, as well as pharmaceutical, life sciences research, and commercial organizations Key FeaturesLearn about healthcare industry challenges and how machine learning can solve themExplore AWS machine learning services and their applications in healthcare and life sciencesDiscover practical coding instructions to implement machine learning for healthcare and life sciencesBook Description While machine learning is not new, it's only now that we are beginning to uncover its true potential in the healthcare and life sciences industry. The availability of real-world datasets and access to better compute resources have helped researchers invent applications that utilize known AI techniques in every segment of this industry, such as providers, payers, drug discovery, and genomics. This book starts by summarizing the introductory concepts of machine learning and AWS machine learning services. You'll then go through chapters dedicated to each segment of the healthcare and life sciences industry. Each of these chapters has three key purposes -- First, to introduce each segment of the industry, its challenges, and the applications of machine learning relevant to that segment. Second, to help you get to grips with the features of the services available in the AWS machine learning stack like Amazon SageMaker and Amazon Comprehend Medical. Third, to enable you to apply your new skills to create an ML-driven solution to solve problems particular to that segment. The concluding chapters outline future industry trends and applications. By the end of this book, you'll be aware of key challenges faced in applying AI to healthcare and life sciences industry and learn how to address those challenges with confidence. What you will learnExplore the healthcare and life sciences industryFind out about the key applications of AI in different industry segmentsApply AI to medical images, clinical notes, and patient dataDiscover security, privacy, fairness, and explainability best practicesExplore the AWS ML stack and key AI services for the industryDevelop practical ML skills using code and AWS servicesDiscover all about industry regulatory requirementsWho this book is for This book is specifically tailored toward technology decision-makers, data scientists, machine learning engineers, and anyone who works in the data engineering role in healthcare and life sciences organizations. Whether you want to apply machine learning to overcome common challenges in the healthcare and life science industry or are looking to understand the broader industry AI trends and landscape, this book is for you. This book is filled with hands-on examples for you to try as you learn about new AWS AI concepts.
Applied Machine Learning For Healthcare And Life Sciences Using Aws
DOWNLOAD
Author : Ujjwal Ratan
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-11-25
Applied Machine Learning For Healthcare And Life Sciences Using Aws written by Ujjwal Ratan and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-25 with Computers categories.
Build real-world artificial intelligence apps on AWS to overcome challenges faced by healthcare providers and payers, as well as pharmaceutical, life sciences research, and commercial organizations Key FeaturesLearn about healthcare industry challenges and how machine learning can solve themExplore AWS machine learning services and their applications in healthcare and life sciencesDiscover practical coding instructions to implement machine learning for healthcare and life sciencesBook Description While machine learning is not new, it's only now that we are beginning to uncover its true potential in the healthcare and life sciences industry. The availability of real-world datasets and access to better compute resources have helped researchers invent applications that utilize known AI techniques in every segment of this industry, such as providers, payers, drug discovery, and genomics. This book starts by summarizing the introductory concepts of machine learning and AWS machine learning services. You'll then go through chapters dedicated to each segment of the healthcare and life sciences industry. Each of these chapters has three key purposes -- First, to introduce each segment of the industry, its challenges, and the applications of machine learning relevant to that segment. Second, to help you get to grips with the features of the services available in the AWS machine learning stack like Amazon SageMaker and Amazon Comprehend Medical. Third, to enable you to apply your new skills to create an ML-driven solution to solve problems particular to that segment. The concluding chapters outline future industry trends and applications. By the end of this book, you'll be aware of key challenges faced in applying AI to healthcare and life sciences industry and learn how to address those challenges with confidence. What you will learnExplore the healthcare and life sciences industryFind out about the key applications of AI in different industry segmentsApply AI to medical images, clinical notes, and patient dataDiscover security, privacy, fairness, and explainability best practicesExplore the AWS ML stack and key AI services for the industryDevelop practical ML skills using code and AWS servicesDiscover all about industry regulatory requirementsWho this book is for This book is specifically tailored toward technology decision-makers, data scientists, machine learning engineers, and anyone who works in the data engineering role in healthcare and life sciences organizations. Whether you want to apply machine learning to overcome common challenges in the healthcare and life science industry or are looking to understand the broader industry AI trends and landscape, this book is for you. This book is filled with hands-on examples for you to try as you learn about new AWS AI concepts.
Applied Machine Learning And High Performance Computing On Aws
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Author : Mani Khanuja
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-12-30
Applied Machine Learning And High Performance Computing On Aws written by Mani Khanuja and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-30 with Computers categories.
Build, train, and deploy large machine learning models at scale in various domains such as computational fluid dynamics, genomics, autonomous vehicles, and numerical optimization using Amazon SageMaker Key FeaturesUnderstand the need for high-performance computing (HPC)Build, train, and deploy large ML models with billions of parameters using Amazon SageMakerLearn best practices and architectures for implementing ML at scale using HPCBook Description Machine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles. This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. Next, you'll learn how to build, train, and deploy large models using distributed training. Later chapters also guide you through deploying models to edge devices using SageMaker and IoT Greengrass, and performance optimization of ML models, for low latency use cases. By the end of this book, you'll be able to build, train, and deploy your own large-scale ML application, using HPC on AWS, following industry best practices and addressing the key pain points encountered in the application life cycle. What you will learnExplore data management, storage, and fast networking for HPC applicationsFocus on the analysis and visualization of a large volume of data using SparkTrain visual transformer models using SageMaker distributed trainingDeploy and manage ML models at scale on the cloud and at the edgeGet to grips with performance optimization of ML models for low latency workloadsApply HPC to industry domains such as CFD, genomics, AV, and optimizationWho this book is for The book begins with HPC concepts, however, it expects you to have prior machine learning knowledge. This book is for ML engineers and data scientists interested in learning advanced topics on using large datasets for training large models using distributed training concepts on AWS, deploying models at scale, and performance optimization for low latency use cases. Practitioners in fields such as numerical optimization, computation fluid dynamics, autonomous vehicles, and genomics, who require HPC for applying ML models to applications at scale will also find the book useful.
Unveiling Technological Advancements And Interdisciplinary Solutions For Pain Care
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Author : Koumpouros, Yiannis
language : en
Publisher: IGI Global
Release Date : 2025-06-24
Unveiling Technological Advancements And Interdisciplinary Solutions For Pain Care written by Koumpouros, Yiannis and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-24 with Medical categories.
Pain management has shifted in recent years, driven by technological advancements and an emphasis on interdisciplinary collaboration. As the understanding of pain becomes more nuanced, recognizing its physical, emotional, and psychological dimensions, healthcare professionals turn to new tools and strategies for enhanced patient outcomes. From wearable monitoring devices and AI-powered diagnostics to integrative approaches involving psychology, physiotherapy, and pharmacology, modern pain care moves beyond traditional methods. Further exploration of the evolving landscape of pain management may reveal how cutting-edge technologies and collaborative care models can reshape the understanding, assessment, and treatment of pain in diverse patient populations. Unveiling Technological Advancements and Interdisciplinary Solutions for Pain Care explores the transformative power of technological innovations, such as wearable sensors, digital tools, and data analysis in understanding unique pain patterns and developing personalized treatment plans. It examines shared decision-making practices, including the importance of building a support system through online communities, fostering emotional well-being, and living a fulfilling life despite chronic pain. This book covers topics such as acute and chronic pain, medical stigma, and personalized healthcare, and is a useful resource for medical and healthcare professionals, engineers, academicians, researchers, and scientists.
Machine Learning In Biological Sciences
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Author : Shyamasree Ghosh
language : en
Publisher: Springer Nature
Release Date : 2022-05-04
Machine Learning In Biological Sciences written by Shyamasree Ghosh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-04 with Medical categories.
This book gives an overview of applications of Machine Learning (ML) in diverse fields of biological sciences, including healthcare, animal sciences, agriculture, and plant sciences. Machine learning has major applications in process modelling, computer vision, signal processing, speech recognition, and language understanding and processing and life, and health sciences. It is increasingly used in understanding DNA patterns and in precision medicine. This book is divided into eight major sections, each containing chapters that describe the application of ML in a certain field. The book begins by giving an introduction to ML and the various ML methods. It then covers interesting and timely aspects such as applications in genetics, cell biology, the study of plant-pathogen interactions, and animal behavior. The book discusses computational methods for toxicity prediction of environmental chemicals and drugs, which forms a major domain of research in the field of biology. It is of relevance to post-graduate students and researchers interested in exploring the interdisciplinary areas of use of machine learning and deep learning in life sciences.
The Role Of Artificial Intelligence In Advancing Applied Life Sciences
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Author : Emara, Tamer
language : en
Publisher: IGI Global
Release Date : 2025-04-29
The Role Of Artificial Intelligence In Advancing Applied Life Sciences written by Emara, Tamer and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-29 with Science categories.
The transformative role of artificial intelligence (AI) is revolutionizing the life sciences sector. AI is being used to accelerate drug discovery, personalize treatments, and improve patient outcomes. AI has demonstrated its potential in optimizing crop yields, enhancing food safety, and addressing global food security challenges. Additionally, AI has applications in climate modeling, species conservation, and pollution monitoring. Discussion of AI implementation in life sciences may stimulate further research and development in AI-driven life science solutions. The Role of Artificial Intelligence in Advancing Applied Life Sciences equips readers with a solid understanding of technology's potential to address complex life science problems. It also discusses the ethical implications and challenges associated with AI implementation in this field. Covering topics such as biomanufacturing, disease identification, and climate change patters, this book is an excellent resource for life scientists, computer scientists, healthcare practitioners, environmentalists, agriculturalists, professionals, researchers, scholars, academicians, and more.
Machine Learning In Biotechnology And Life Sciences
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Author : Saleh Alkhalifa
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-01-28
Machine Learning In Biotechnology And Life Sciences written by Saleh Alkhalifa and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-28 with Mathematics categories.
Explore all the tools and templates needed for data scientists to drive success in their biotechnology careers with this comprehensive guide Key FeaturesLearn the applications of machine learning in biotechnology and life science sectorsDiscover exciting real-world applications of deep learning and natural language processingUnderstand the general process of deploying models to cloud platforms such as AWS and GCPBook Description The booming fields of biotechnology and life sciences have seen drastic changes over the last few years. With competition growing in every corner, companies around the globe are looking to data-driven methods such as machine learning to optimize processes and reduce costs. This book helps lab scientists, engineers, and managers to develop a data scientist's mindset by taking a hands-on approach to learning about the applications of machine learning to increase productivity and efficiency in no time. You'll start with a crash course in Python, SQL, and data science to develop and tune sophisticated models from scratch to automate processes and make predictions in the biotechnology and life sciences domain. As you advance, the book covers a number of advanced techniques in machine learning, deep learning, and natural language processing using real-world data. By the end of this machine learning book, you'll be able to build and deploy your own machine learning models to automate processes and make predictions using AWS and GCP. What you will learnGet started with Python programming and Structured Query Language (SQL)Develop a machine learning predictive model from scratch using PythonFine-tune deep learning models to optimize their performance for various tasksFind out how to deploy, evaluate, and monitor a model in the cloudUnderstand how to apply advanced techniques to real-world dataDiscover how to use key deep learning methods such as LSTMs and transformersWho this book is for This book is for data scientists and scientific professionals looking to transcend to the biotechnology domain. Scientific professionals who are already established within the pharmaceutical and biotechnology sectors will find this book useful. A basic understanding of Python programming and beginner-level background in data science conjunction is needed to get the most out of this book.
Ai And Blockchain In Healthcare
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Author : Bipin Kumar Rai
language : en
Publisher: Springer Nature
Release Date : 2023-04-30
Ai And Blockchain In Healthcare written by Bipin Kumar Rai and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-30 with Computers categories.
This book presents state-of-the-art blockchain and AI advances in health care. Healthcare service is increasingly creating the scope for blockchain and AI applications to enter the biomedical and healthcare world. Today, blockchain, AI, ML, and deep learning are affecting every domain. Through its cutting-edge applications, AI and ML are helping transform the healthcare industry for the better. Blockchain is a decentralization communication platform that has the potential to decentralize the way we store data and manage information. Blockchain technology has potential to reduce the role of middleman, one of the most important regulatory actors in our society. Transactions are simultaneously secure and trustworthy due to the use of cryptographic principles. In recent years, blockchain technology has become very trendy and has penetrated different domains, mostly due to the popularity of cryptocurrencies. One field where blockchain technology has tremendous potential is health care, due to the need for a more patient-centric approach in healthcare systems to connect disparate systems and to increase the accuracy of electronic healthcare records (EHRs).
Applied Data Science
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Author : Martin Braschler
language : en
Publisher: Springer
Release Date : 2019-06-13
Applied Data Science written by Martin Braschler and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-13 with Computers categories.
This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science:first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.
Machine Learning And Systems Biology In Genomics And Health
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Author : Shailza Singh
language : en
Publisher: Springer Nature
Release Date : 2022-02-04
Machine Learning And Systems Biology In Genomics And Health written by Shailza Singh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-04 with Science categories.
This book discusses the application of machine learning in genomics. Machine Learning offers ample opportunities for Big Data to be assimilated and comprehended effectively using different frameworks. Stratification, diagnosis, classification and survival predictions encompass the different health care regimes representing unique challenges for data pre-processing, model training, refinement of the systems with clinical implications. The book discusses different models for in-depth analysis of different conditions. Machine Learning techniques have revolutionized genomic analysis. Different chapters of the book describe the role of Artificial Intelligence in clinical and genomic diagnostics. It discusses how systems biology is exploited in identifying the genetic markers for drug discovery and disease identification. Myriad number of diseases whether be infectious, metabolic, cancer can be dealt in effectively which combines the different omics data for precision medicine. Major breakthroughs in the field would help reflect more new innovations which are at their pinnacle stage. This book is useful for researchers in the fields of genomics, genetics, computational biology and bioinformatics.