Data science, AI, and machine learning in drug development
Author(s)
Bibliographic Information
Data science, AI, and machine learning in drug development
（Chapman & Hall/CRC biostatistics series）
CRC Press, Chapman & Hall Book, c2023
- : hbk
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
Provides a comprehensive review of challenges and opportunities as related to the applications of big data, AI, and machine learning 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 detailed description of solutions suitable for practitioners with limited data science expertise
Table of Contents
1 Transforming Pharma with Data Science, AI and Machine Learning 2 Regulatory Perspective on Big Data, AI, and Machining Learning 3 Building an Agile and Scalable Data Science Organization 4 AI and Machine Learning in Drug Discovery 5 Predicting Anti-Cancer Synergistic Activity Through Machine Learning and Natural Language Processing 6 AI-Enabled Clinical Trials 7 Machine Learning for Precision Medicine 8 Reinforcement Learning in Personalized Medicine 9 Leveraging Machine Learning, Natural Language Processing, and Deep Learning in Drug Safety and Pharmacovigilance 10 Intelligent Manufacturing and Supply of Biopharmaceuticals 11 Reinventing Medical Affairs in the Era of Big Data and Analytics 12 Deep Learning with Electronic Health Record 13 Real-World Evidence for Treatment Access and Payment Decisions
by "Nielsen BookData"
