Fundamentals of Clinical Data Science

Author: Pieter Kubben,Michel Dumontier, Andre Dekker

Published in: Springer

Release Year: 2019

ISBN: 978-3-319-99713-1

Pages: 217

Edition: 1st

File Size: 7 MB

File Type: pdf

Language: English

Description of Fundamentals of Clinical Data Science

In the era of eHealth and personalized medicine, “big data” and “machine learning” are increasingly becoming part of the medical world. Algorithms are capable of supporting diagnostic and therapeutic processes and offer added value for both health-care professionals and patients. The field of big data, machine learning, deep learning, and algorithm development and validation is often referred to as “data science,” and “data scientist” was mentioned in Harvard Business Review as “the sexiest job of the 21th century” ( job-of-the-21st-century). A commonly used visual representation of the field is Drew Conway’s Venn diagram (Fig. 1), which describes data science as a mix of content expertise, methodological knowledge, and IT skills.
Unfortunately, most healthcare professionals still consider the field of clinical data science as highly technical and something “for the IT whizzkids.” That leaves many interesting and valuable opportunities unexplored and could even contribute to serious flaws in developed algorithms. Chen and Asch described machine learning’s “peak of inflated expectations” and suggest that “we can soften a subsequent crash into a ‘trough of disillusionment’ by fostering a stronger appreciation of the
technology’s capabilities and limitations” (Chen and Asch 2017). They conclude that “combining machine-learning software with the best human clinician ‘hardware’ will permit delivery of care that outperforms what either can do alone.”
We could not agree more. Fundamentals of Clinical Data Science book is for you, the healthcare professional and “best human clinician hardware” who would like to embrace the field of clinical data science but who is still looking for a resource that explains the topic in nonengineering terminology. Fundamentals of Clinical Data Science book’s promise is “no math, no code.” It contains three sections that help you understand the transformation of data to model and to applications. It should be sufficient to give you a decent grasp on the topic for understanding and a solid foundation if you are to continue with active mastery of the field by taking programming courses online or in a classroom setting. Either way, we want you to get aboard.
Our thanks go to the NFU Citrienfonds who made it financially possible to publish Fundamentals of Clinical Data Science e-book as open access. Citrienfonds of the NFU and ZonMw helps to develop sustainable solutions in Dutch healthcare to all authors for their valuable time and contributions, to Studio Piranha for the website, and to Springer for their help in the publishing process.
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