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001437813 001__ 1437813
001437813 003__ OCoLC
001437813 005__ 20230309004234.0
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001437813 008__ 210702s2021\\\\caua\\\\ob\\\\001\0\eng\d
001437813 019__ $$a1260347533$$a1266810114
001437813 020__ $$a9781484270868$$q(electronic bk.)
001437813 020__ $$a148427086X$$q(electronic bk.)
001437813 020__ $$z9781484270851
001437813 020__ $$z1484270851
001437813 0247_ $$a10.1007/978-1-4842-7086-8$$2doi
001437813 035__ $$aSP(OCoLC)1258659541
001437813 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dOCLCO$$dN$T$$dOCLCF$$dDCT$$dUKAHL$$dOCLCQ$$dOCLCO$$dK6U$$dOCLCQ
001437813 049__ $$aISEA
001437813 050_4 $$aR859.7.A78$$bA47 2021
001437813 08204 $$a610.285/63$$223
001437813 1000_ $$aAnshik,$$eauthor.
001437813 24510 $$aAI for healthcare with Keras and Tensorflow 2.0 :$$bdesign, develop, and deploy machine learning models using healthcare data /$$cAnshik.
001437813 264_1 $$a[Berkeley] :$$bApress,$$c[2021]
001437813 264_4 $$c©2021
001437813 300__ $$a1 online resource :$$billustrations (some color)
001437813 336__ $$atext$$btxt$$2rdacontent
001437813 337__ $$acomputer$$bc$$2rdamedia
001437813 338__ $$aonline resource$$bcr$$2rdacarrier
001437813 347__ $$atext file
001437813 347__ $$bPDF
001437813 504__ $$aIncludes bibliographical references and index.
001437813 5050_ $$aChapter 1: Healthcare Market: A Primer -- Chapter 2: Introduction and Setup -- Chapter 3: Predicting Hospital Readmission by Analyzing Patient EHR Records -- Chapter 4: Predicting Medical Billing Codes from Clinical Notes -- Chapter 5: Extracting Structured Data from Receipt Images Using a Graph Convolutional Network -- Chapter 6: Handling Availability of Low-Training Data in Healthcare -- Chapter 7: Federated Learning and Healthcare. -- Chapter 8: Medical Imaging -- Chapter 9: Machines Have All the Answers, Except What's the Purpose of Life? -- Chapter 10: You Need an Audience Now.
001437813 506__ $$aAccess limited to authorized users.
001437813 520__ $$aLearn how AI impacts the healthcare ecosystem through real-life case studies with TensorFlow 2.0 and other machine learning (ML) libraries. This book begins by explaining the dynamics of the healthcare market, including the role of stakeholders such as healthcare professionals, patients, and payers. Then it moves into the case studies. The case studies start with EHR data and how you can account for sub-populations using a multi-task setup when you are working on any downstream task. You also will try to predict ICD-9 codes using the same data. You will study transformer models. And you will be exposed to the challenges of applying modern ML techniques to highly sensitive data in healthcare using federated learning. You will look at semi-supervised approaches that are used in a low training data setting, a case very often observed in specialized domains such as healthcare. You will be introduced to applications of advanced topics such as the graph convolutional network and how you can develop and optimize image analysis pipelines when using 2D and 3D medical images. The concluding section shows you how to build and design a closed-domain Q & A system with paraphrasing, re-ranking, and strong QnA setup. And, lastly, after discussing how web and server technologies have come to make scaling and deploying easy, an ML app is deployed for the world to see with Docker using Flask. By the end of this book, you will have a clear understanding of how the healthcare system works and how to apply ML and deep learning tools and techniques to the healthcare industry. You will: Get complete, clear, and comprehensive coverage of algorithms and techniques related to case studies Look at different problem areas within the healthcare industry and solve them in a code-first approach Explore and understand advanced topics such as multi-task learning, transformers, and graph convolutional networks Understand the industry and learn ML.
001437813 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed July 23, 2021).
001437813 650_0 $$aArtificial intelligence$$xMedical applications.
001437813 650_0 $$aMachine learning$$xDevelopment.
001437813 650_6 $$aIntelligence artificielle en médecine.
001437813 650_6 $$aApprentissage automatique$$xDéveloppement.
001437813 655_0 $$aElectronic books.
001437813 77608 $$iPrint version:$$z1484270851$$z9781484270851$$w(OCoLC)1243349948
001437813 852__ $$bebk
001437813 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-7086-8$$zOnline Access$$91397441.1
001437813 909CO $$ooai:library.usi.edu:1437813$$pGLOBAL_SET
001437813 980__ $$aBIB
001437813 980__ $$aEBOOK
001437813 982__ $$aEbook
001437813 983__ $$aOnline
001437813 994__ $$a92$$bISE