Reversing Cadasil Syndrome: Overcoming Cravings the Raw Vegan Plant-Based Detoxification & Regeneration Workbook for Healing Patients. Volume 3, Central Health
This new volume draws from provocative discussions on the urban social contract among policy makers, researchers, public intellectuals, and citizens at the 2015 UIC Urban Forum. Michael A. Pagano presents papers that emphasize political agreements, disagreements, challenges, and controversies on health, energy, and environmental policies. Authors explore the substantive and philosophical changes in the urban social contract and offer proposals for remaking it in the new century. Topics range from big-picture analyses to specifics covering areas like public services, the smart cities movement, and greening strategies. Contributors: Alba Alexander, Megan Houston, Dennis R. Judd, Cynthia Klein-Banai, William C. Kling, Howard A. Learner, David A. McDonald, David C. Perry, Emily Stiehl, Anthony Townsend, Natalia Villamizar-Duarte, and Moira Zellner.
Автор: Wang, Sun-chong (national Central Univ, Taiwan) Название: Clinical herbal prescriptions: principles and practices of herbal formulations from deep learning health insurance herbal prescription big data ISBN: 9813278242 ISBN-13(EAN): 9789813278240 Издательство: World Scientific Publishing Рейтинг: Цена: 179520.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Since AlphaGo defeated Ke Jie (who was then ranked 1st among all human players worldwide) May 2017, the art of Go (otherwise known as Weiqi) has entered a new era. Similarly, if we apply artificial intelligence (AI) to herbal medicine, the art of herbal prescription can experience a game change too. The author of this book has done exactly that, and via reverse engineering of the trained AI, the book details how one can compose herbal prescriptions from scratch.As artificial intelligence (AI) technologies outperform humans in such tasks as image/voice recognition and language translation, mastering of concentrated herbal extract granules (CHEG) prescription composition by AI is not a fiction, provided large quantities of high-quality CHEG prescription data are available. Thanks to the 340 million records of modern Western medicine diagnoses and corresponding CHEG prescriptions in the National Health Insurance Reimbursement Database (Taiwan) recorded in the decade between 2004 and 2013, the book is based on the results of applying state-of-the-art deep learning technologies to the CHEG prescription big data.
