Biomedical Research Data in the Age of AI
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Beschreibung
This book offers an alternative to faith-dependent analytical approaches, explaining how original data can be transformed into cogent and compelling interpretations with analytical techniques that are straightforward and accessible to biomedical scientists. Some data-related topics covered in the book are the aesthetics of data or how beauty in data inspires, erroneous data by fraud or honest mistake, the difference between experimental and observational data, reproducibility of data, and the implications of focusing on original data for peer review.
By considering these various subjects, the author has synthesized a philosophy to help students develop an effective and appropriate sensibility. It can serve as a guide for biomedical research investigators in their studies, assist practitioners in making sense of complex mechanisms for patient benefit, and for business professionals who may learn from a thoughtful consideration of biomedical science. This book is intentionally accessible for those without an extensive biomedical science background, and hopes to motivate readers to expand their data literacy and comprehension, in the age of AI.
This book offers an alternative to faith-dependent analytical approaches, explaining how original data can be transformed into cogent and compelling interpretations with analytical techniques that are straightforward and accessible to biomedical scientists. Some data-related topics covered in the book are the aesthetics of data or how beauty in data inspires, erroneous data by fraud or honest mistake, the difference between experimental and observational data, reproducibility of data, and the implications of focusing on original data for peer review.
By considering these various subjects, the author has synthesized a philosophy to help students develop an effective and appropriate sensibility. The book can serve as a guide for biomedical research investigators in their studies, assist practitioners in making sense of complex mechanisms for patient benefit, and for business professionals who may learn from a thoughtful consideration of biomedical science. This book is intentionally accessible for those without an extensive biomedical science background, and hopes to motivate readers to expand their data literacy and comprehension, in the age of AI.
Explores research analysis of biomedical data through AI machine learning Provide readers with tools to understand biomedical science and convert biomedical research into a clinical sensibility Accessible scientific information about AI and data analysis
Autor*in
David Kaplan
Themen in »Biomedical Research Data in the Age of AI«
information specialists, original data, AI, Data quality, predictive modeling, biomedical scientists aesthetics of data, observational, experimental Machine learning (artificial intelligence), statistics Data Collection, Data Quality, Bayesian statistics Erroneous Data, Converting Data into Interpretation peer review, computer-generation calculations