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Data science, AI, and machine learning in drug development

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Data science, AI, and machine learning in drug development/ edited by Harry Yang.
other author:	Yang, Harry.
Published:	Boca Raton, FL :Chapman & Hall/CRC Press, : 2023.,
Description:	1 online resource (xiv, 320 p.)
Subject:	Drug development - Data processing. -
Online resource:	https://www.taylorfrancis.com/books/9781003150886
ISBN:	9781003150886

Data science, AI, and machine learning in drug development
Data science, AI, and machine learning in drug development[electronic resource] /edited by Harry Yang. - 1st ed. - Boca Raton, FL :Chapman & Hall/CRC Press,2023. - 1 online resource (xiv, 320 p.) - Chapman & Hall/CRC bostatistics series. - Chapman & Hall/CRC biostatistics series..

Includes bibliographical references and index.

Chapter 1 Transforming Pharma with Data Science, AI and Machine Learning -- Chapter 2 Regulatory Perspective on Big Data, AI, and Machining Learning -- Chapter 3 Building an Agile and Scalable Data Science Organization -- Chapter 4 AI and Machine Learning in Drug Discovery -- Chapter 5 Predicting Anti-Cancer Synergistic Activity Through Machine Learning and Natural Language Processing -- Chapter 6 AI-Enabled Clinical Trials -- Chapter 7 Machine Learning for Precision Medicine -- Chapter 8 Reinforcement Learning in Personalized Medicine -- Chapter 9 Leveraging Machine Learning, Natural Language Processing, and Deep Learning in Drug Safety and Pharmacovigilance -- Chapter 10 Intelligent Manufacturing and Supply of Biopharmaceuticals -- Chapter 11 Reinventing Medical Affairs in the Era of Big Data and Analytics -- Chapter 12 Deep Learning with Electronic Health Record -- Chapter 13 Real-World Evidence for Treatment Access and Payment Decisions.

The confluence of big data, artificial intelligence (AI), and machine learning (ML) has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change. Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R & D, emerging applications of big data, AI and ML in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations. Features Provides a comprehensive review of challenges and opportunities as related to the applications of big data, AI, and ML in the entire spectrum of drug R & D Discusses regulatory developments in leveraging big data and advanced analytics in drug review and approval Offers a balanced approach to data science organization build Presents real-world examples of AI-powered solutions to a host of issues in the lifecycle of drug development Affords sufficient context for each problem and provides a detailed description of solutions suitable for practitioners with limited data science expertise

ISBN: 9781003150886Subjects--Topical Terms:

964848
Drug development
--Data processing.

LC Class. No.: RM301.25

Dewey Class. No.: 615.1/900285

Data science, AI, and machine learning in drug development
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505 0 $a Chapter 1 Transforming Pharma with Data Science, AI and Machine Learning -- Chapter 2 Regulatory Perspective on Big Data, AI, and Machining Learning -- Chapter 3 Building an Agile and Scalable Data Science Organization -- Chapter 4 AI and Machine Learning in Drug Discovery -- Chapter 5 Predicting Anti-Cancer Synergistic Activity Through Machine Learning and Natural Language Processing -- Chapter 6 AI-Enabled Clinical Trials -- Chapter 7 Machine Learning for Precision Medicine -- Chapter 8 Reinforcement Learning in Personalized Medicine -- Chapter 9 Leveraging Machine Learning, Natural Language Processing, and Deep Learning in Drug Safety and Pharmacovigilance -- Chapter 10 Intelligent Manufacturing and Supply of Biopharmaceuticals -- Chapter 11 Reinventing Medical Affairs in the Era of Big Data and Analytics -- Chapter 12 Deep Learning with Electronic Health Record -- Chapter 13 Real-World Evidence for Treatment Access and Payment Decisions.
520 $a The confluence of big data, artificial intelligence (AI), and machine learning (ML) has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change. Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R & D, emerging applications of big data, AI and ML in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations. Features Provides a comprehensive review of challenges and opportunities as related to the applications of big data, AI, and ML in the entire spectrum of drug R & D Discusses regulatory developments in leveraging big data and advanced analytics in drug review and approval Offers a balanced approach to data science organization build Presents real-world examples of AI-powered solutions to a host of issues in the lifecycle of drug development Affords sufficient context for each problem and provides a detailed description of solutions suitable for practitioners with limited data science expertise
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830 0 $a Chapman & Hall/CRC biostatistics series. $3 971089
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