Bookedited by Harry Yang.
Summary: "The confluence of big data, AI, and machine learning 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 machine learning in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations"-- Provided by publisher.
Contents:
Transforming Pharma with Data Science, AI and Machine Learning
Regulatory Perspective on Big Data, AI, and Machining Learning
Building an Agile and Scalable Data Science Organization
AI and Machine Learning in Drug Discovery
Predicting Anti-Cancer Synergistic Activity Through Machine Learning and Natural Language Processing
AI-Enabled Clinical Trials
Machine Learning for Precision Medicine
Reinforcement Learning in Personalized Medicine
Leveraging Machine Learning, Natural Language Processing, and Deep Learning in Drug Safety and Pharmacovigilance
Intelligent Manufacturing and Supply of Biopharmaceuticals
Reinventing Medical Affairs in the Era of Big Data and Analytics
Deep Learning with Electronic Health Record
Real-World Evidence for Treatment Access and Payment Decisions.