Data science, AI, and machine learning in drug development / Harry Yang.
By: Yang, Harry [author.].
Series: Chapman & Hall/CRC biostatistics series.Publisher: Boca Raton : CRC Press, ©2023Edition: First edition.Description: 320 p.Content type: text Media type: unmediated Carrier type: volumeISBN: 9780367708078; 9780367714413.Subject(s): Drug development -- Data processing | Artificial intelligence | Machine learningGenre/Form: Print books.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"--| Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|
| On Shelf | RM301.25 .Y3625 2023 (Browse shelf) | Available | AU00000000019818 |
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| RM301.25 .P754 2020 Principles of research design and drug literature evaluation / | RM301.25 .S73 2015 Statistical methods in drug combination studies / | RM301.25 .V47 2022 Making medicine : surprising stories from the history of drug discovery / | RM301.25 .Y3625 2023 Data science, AI, and machine learning in drug development / | RM301.25 .Z3625 2023 Drug development for rare diseases / | RM301.27 .G65 2014 Bad pharma : how drug companies mislead doctors and harm patients / | RM301.27 .K39 2015 Statistical thinking for non-statisticians in drug regulation / |
Includes bibliographical references and index.
"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"--