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Книги з теми "Personalized prediction"

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Автор: Grafiati

Опубліковано: 1 червня 2024

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1

Grech, Godfrey, and Iris Grossman, eds. Preventive and Predictive Genetics: Towards Personalised Medicine. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15344-5.

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2

Olga, Golubnitschaja, ed. Predictive diagnostics and personalized treatment: Dream or reality. Hauppauge, NY: Nova Science Publishers, 2009.

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3

Podbielska, Halina, and Marko Kapalla, eds. Predictive, Preventive, and Personalised Medicine: From Bench to Bedside. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34884-6.

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4

Chaari, Lotfi, ed. Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11800-6.

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5

Chaari, Lotfi, ed. Digital Health in Focus of Predictive, Preventive and Personalised Medicine. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49815-3.

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6

Berliner, Leonard, and Heinz U. Lemke, eds. An Information Technology Framework for Predictive, Preventive and Personalised Medicine. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12166-6.

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7

Precision Medicine: Prediction, Prevention with Personalization. Taylor & Francis Group, 2018.

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8

Mansnérus, Juli, Raimo Lahti, and Amanda Blick, eds. Personalized medicine: Legal and ethical challenges. University of Helsinki, Faculty of Law, 2020. http://dx.doi.org/10.31885/9789515169419.

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Анотація:

This anthology deals with the legal and ethical challenges regarding personalized (precision) medicine and healthcare. It can also be regarded as the final report of a research project on the legal and ethical aspects of personalized medicine. It complements the reported results of the consortium ‘Personalised medicine to predict and prevent Type 1 Diabetes (P4 Diabetes)’ which were briefly presented in the booklet entitled ‘Better, Smarter, Now: Personalised Health – From Genes to Society (pHealth)’, Academy of Finland, Helsinki 2019. The articles of this anthology are not limited to the aspects of predicting and preventing Type 1 Diabetes only, as the name of the consortium would suggest. The list of participating researchers indicates that many-sided medical expertise was represented in the consortium and, in addition, computational data analysis as well as legal and ethical issues were covered by the participating sites of research. A comprehensive examination of the issues of personalized medicine requires multidisciplinary approaches. In this anthology, the legally and ethically oriented mainstream of writings has been complemented with an article of a computer scientist in order to recognize the possibilities and challenges of machine learning when interpreting the patient’s need for help. It is our hope that this anthology would be useful both for the academic community and for the decision-makers in the fields of healthcare and (personalized) medicine. It is also advisable that the anthology would give an impetus for further research activity in these new spheres of medical law and biolaw.

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9

Wunsch, Hannah, and Andrew A. Kramer. The role and limitations of scoring systems. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0028.

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Scoring systems for critically-ill patients provide a measure of the severity of illness of patients admitted to intensive care units (ICUs). They are primarily based on patient characteristics, physiological derangement, and/or clinical assessments. Severity scores themselves allow for risk-adjusting outcomes, but they can also be used to provide a prediction of the overall risk of death, length of stay, or other outcome for critically ill patients. This allows for comparison of outcomes between different cohorts of patients or between observed and predicted ICU performance. There are a number of general ICU scoring systems that are in use. All scoring systems have limitations. Future scoring systems may include prediction of longer-term outcomes, and assimilation of granular data temporally and at the molecular level that could result in more personalized severity scores to help guide individual care decisions.

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10

Personalized Predictive Modelling in Type1 Diabetes. Elsevier Science & Technology Books, 2017.

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11

Personalized Predictive Modeling in Type 1 Diabetes. Elsevier, 2018. http://dx.doi.org/10.1016/c2015-0-04195-8.

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12

Fotiadis, Dimitrios I., Eleni I. Georga, and Stelios K. Tigas. Personalized Predictive Modeling in Type 1 Diabetes. Elsevier Science & Technology Books, 2017.

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13

Grech, Godfrey, and Iris Grossman. Preventive and Predictive Genetics: Towards Personalised Medicine. Springer London, Limited, 2015.

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14

Grech, Godfrey, and Iris Grossman. Preventive and Predictive Genetics: Towards Personalised Medicine. Springer International Publishing AG, 2015.

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15

Grech, Godfrey, and Iris Grossman. Preventive and Predictive Genetics: Towards Personalised Medicine. Springer, 2016.

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16

Golubnitschaja, O. Predictive Diagnostics and Personalized Treatment: Dream or Reality. Nova Science Publishers, Incorporated, 2009.

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17

Healthcare Overview Advances in Predictive Preventive and Personalised Medicine. Springer, 2012.

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18

Chaari, Lotfi. Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine. Springer, 2019.

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19

Chaari, Lotfi. Digital Health in Focus of Predictive, Preventive and Personalised Medicine. Springer, 2020.

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20

Lemke, Heinz U., and Leonard Berliner. Information Technology Framework for Predictive, Preventive and Personalised Medicine: A Use-Case with Hepatocellular Carcinoma. Springer, 2015.

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21

Lemke, Heinz U., and Leonard Berliner. Information Technology Framework for Predictive, Preventive and Personalised Medicine: A Use-Case with Hepatocellular Carcinoma. Springer International Publishing AG, 2015.

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22

Lemke, Heinz U., and Leonard Berliner. An Information Technology Framework for Predictive, Preventive and Personalised Medicine: A Use-Case with Hepatocellular Carcinoma. Springer, 2016.

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23

Martha, Moonblade. Baby Shower Guest Book: Pink Glittery Style Alternative Theme for Guests to Sign in with Personalized Address Space, Write Predictions. Independently Published, 2022.

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24

Hood, Leroy, and Nathan Price. Age of Scientific Wellness: Why the Future of Medicine Is Personalized, Predictive, Data-Rich, and in Your Hands. Harvard University Press, 2023.

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25

Age of Scientific Wellness: Why the Future of Medicine Is Personalized, Predictive, Data-Rich, and in Your Hands. Harvard University Press, 2023.

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26

Rovner, Joshua. Pathologies of Intelligence Producer-Consumer Relations. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190846626.013.272.

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Анотація:

The shift in America’s national security priorities has significantly changed the foreign intelligence needs of US policymakers in recent years. Due to the substantial rise of transnational threats, intelligence requirements have become increasingly numerous and varied, necessitating ever closer communication between consumers and producers to facilitate the production of relevant and timely intelligence. Producer-consumer relations is the glue which pulls together the intelligence cycle; for what happens at the interface of policy and intelligence ultimately determines the success or failure of the entire intelligence endeavor. Efforts to reform intelligence analysis have been motivated by the assumption that accurate analysis naturally leads to effective policy decisions. From this perspective, computational resources have primarily been devoted to the collection and assessment of empirical data in an effort to provide consumers with increasingly accurate predictions. The perennial issues facing the intelligence community can be roughly summarized as follows: the intelligence professional must guard against politicization and uphold his analytical integrity while at the same time maintaining close enough contact with policymakers to provide personalized and relevant intelligence support. Scholars argue that what the producer-consumer relationship needs is not radical change but some amelioration. The general reform objective should be to deepen the incorporation of intelligence throughout the policymaking process, to improve the two-way understanding of policy requirements, and to ensure that the intelligence community maximizes and maintains its unique expertise.

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27

Riley, Richard D., Danielle van der Windt, Peter Croft, and Karel G. M. Moons, eds. Prognosis Research in Health Care. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780198796619.001.0001.

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Анотація:

What is going to happen to me, doctor?’ ‘What outcomes am I likely to experience?’ ‘Will this treatment work for me?’ Prognosis—forecasting the future—has always been a part of medical practice and caring for the sick. In modern healthcare it now has a new importance, with large financial investments being made to personalize clinical decisions and tailor treatment strategies to improve individual health outcomes based on prognostic information. Prognosis research—the study of future outcomes in people with a particular health condition—provides the critical evidence for obtaining, evaluating, and implementing prognostic information within modern healthcare. This new book, written and edited by experts in the field, including clinicians, epidemiologists, statisticians, and other healthcare professionals, is a comprehensive and unified account of prognosis research in the broadest sense. It explains the concepts behind prognosis in medical practice and prognosis research, and provides a practical foundation for those developing, conducting, interpreting, synthesizing, and appraising prognosis studies. It recommends a framework of four basic prognosis research types, pioneered by the PROGRESS group, and provides explicit guidance on the conduct, analysis, and reporting of prognosis studies for each type. Key topics are overall prognosis in clinically relevant populations; prognostic factors associated with changes in prognosis across individuals; prognostic models for individual outcome risk prediction; and predictors of treatment effects. Examples are given of the impact of prognosis research across a broad range of healthcare topics, and the book also signals the latest developments in prognosis research, including systematic reviews and meta-analysis of prognosis studies, and the use of electronic health records and machine learning in prognosis research.

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28

Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.

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Анотація:

Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip its readers with a comprehensive understanding of AI and its subsets, machine learning and deep learning, with a particular emphasis on neural networks. It is designed for novices venturing into the field, as well as experienced learners who desire to solidify their knowledge base or delve deeper into advanced topics. In Chapter 1, we provide a thorough introduction to the world of AI, exploring its definition, historical trajectory, and categories. We delve into the applications of AI, and underscore the ethical implications associated with its proliferation. Chapter 2 introduces machine learning, elucidating its types and basic algorithms. We examine the practical applications of machine learning and delve into challenges such as overfitting, underfitting, and model validation. Deep learning and neural networks, an integral part of AI, form the crux of Chapter 3. We provide a lucid introduction to deep learning, describe the structure of neural networks, and explore forward and backward propagation. This chapter also delves into the specifics of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). In Chapter 4, we outline the steps to train neural networks, including data preprocessing, cost functions, gradient descent, and various optimizers. We also delve into regularization techniques and methods for evaluating a neural network model. Chapter 5 focuses on specialized topics in neural networks such as autoencoders, Generative Adversarial Networks (GANs), Long Short-Term Memory Networks (LSTMs), and Neural Architecture Search (NAS). In Chapter 6, we illustrate the practical applications of neural networks, examining their role in computer vision, natural language processing, predictive analytics, autonomous vehicles, and the healthcare industry. Chapter 7 gazes into the future of AI and neural networks. It discusses the current challenges in these fields, emerging trends, and future ethical considerations. It also examines the potential impacts of AI and neural networks on society. Finally, Chapter 8 concludes the book with a recap of key learnings, implications for readers, and resources for further study. This book aims not only to provide a robust theoretical foundation but also to kindle a sense of curiosity and excitement about the endless possibilities AI and neural networks offer. The journ

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29

Press, Benita. Baby Shower Guest Book: Green Personalized Keepsake Baby Shower Memory Book for Guests to Sign with Name, Address, Relationship, Advice for Parents, Wishes and Predictions and Gift Log Tracking. Independently Published, 2021.

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30

Press, Benita. Baby Shower Guest Book: Boho Rainbow Personalized Keepsake Baby Shower Memory Book for Guests to Sign with Name, Address, Relationship, Advice for Parents, Wishes and Predictions and Gift Log Tracking. Independently Published, 2021.

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31

Moore, Martha. It's a Boy Baby Shower Guest Book: Blue Teddybear Style for Guests to Sign in with Personalized with Address Space, Advice to Parents,Predictions, Gift Tracker and Photo Pages, Cute Lettering Design. Independently Published, 2022.

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32

Moore, Martha. Baby Shower Guest Book: Pink Glittery Style Alternative Theme for Guests to Sign in with Personalized Address Space, Write Predictions, Messages and Advice to Parents, Blank Photo Pages , Invitation List and Create a Memorable Keepsake. Independently Published, 2022.

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33

PUBLISHING, Skanou. Baby Shower Guest Book: It's a Boy Baby Bear Guestbook, Teddy Bear Theme, Baby Predictions, Will Wishes for the Baby and Advice for Parents, Guests Sign in Personalized with Gift Log Tracker, Keepsake Pictures Pages. Independently Published, 2022.

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