Data Science in Critical Care, An Issue of Critical Care Clinics, 1st Edition
特長
-
Contains 15 relevant, practice-oriented topics including AI and the imaging revolution; designing “living, breathing clinical trials: lessons learned from the COVID-19 pandemic; the patient or the population: knowing the limitations of our data to make smart clinical decisions; weighing the cost vs. benefit of AI in healthcare; and more.
-
Provides in-depth clinical reviews on data science in critical care, offering actionable insights for clinical practice.
-
Presents the latest information on this timely, focused topic under the leadership of experienced editors in the field. Authors synthesize and distill the latest research and practice guidelines to create clinically significant, topic-based reviews.
著者情報
| ISBN Number | 9780443181931 |
|---|---|
| Description Author List | Edited by Rishikesan Kamaleswaran, PhD, Director of Translational Clinical Informatics, Assistant Professor, Departments of Biomedical Informatics, Pediatrics, and Emergency Medicine Emory University School of Medicine, Department of Biomedical Engineering, Georgia Institute of Technology and Andre L. Holder, MD, MS, Assistant Professor of Medicine, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University School of Medicine, Grady Memorial Hospital |
| Copyright Year | 2023 |
| Edition Number | 1 |
| Format | Book |
| Trim | 152w x 229h (6.00" x 9.00") |
| Imprint | Elsevier |
| Page Count | 240 |
| Publication Date | 14 Sep 2023 |
| Stock Status | IN STOCK |
eBooks : Built for busy schedules & tailored for your goals.
Affordable knowledge, built for you
Get the resources you need-often at a lower cost than print. Quality content designed to support your goals, without stretching your budget.
Seamless access wherever you are
Always in sync
Tools the make learning stick
Your eBook is ready whenever you are!
1. Check your email for your access code.
2. Sign into or create your VitalSource account and redeem your code.
3. Open your eBook - ready whenever you are!
FAQ
The access code for your new eBook will be sent in your order confirmation email. Your code can also be accessed in your My Account section on the Elsevier webshop. If you do not receive your code within a few minutes, please check your spam folder.
Step-by-step guidance on how to download Bookshelf and also redeem your code can be found here.
The access code for your new eBook does not expire. However, we always suggest redeeming immediately after purchase to start experiencing the benefits of and insights from your purchase. Important to note - the code provided is a single use code and only valid for the edition you purchase. It does not provide access to past nor future editions of the title.
You will have unlimited access to your eBook on the device to which it was downloaded.
Discover the various learning features that our eBooks offer on the Bookshelf® Reader! For example, you can highlight different text passages, create notes and flashcards, have the text read to you, etc. Particularly practical: You can also use your eBooks offline. More information on the learning functions can be found on the Vitalsource page.
Quality is our top priority. That's why we collaborate with the leading eBook reader provider VitalSource. VitalSource has its own eBook reader Bookshelf®, which you can easily download. This reader is very user-friendly and offers more features than other standard readers. For example, you can highlight different text passages, create notes and flashcards, have the text read to you, etc. Particularly practical: You can also use your eBooks offline. More information can be found on the Vitalsource page.
Elsevier offers its eBooks in ePub format, as we believe this format is best suited to display our content ideally on as many devices as possible.
You can return your eBook within 13 days of purchase. eBooks that have been partially printed or flipped through more than 15% are excluded from returns.
Any questions ?
Predictive Modeling Using Artificial Intelligence and Machine Learning Algorithms on Electronic Health Record Data: Advantages and Challenges
Machine Learning of Physiologic Waveforms and Electronic Health Record Data: A Large Perioperative Data Set of High-Fidelity Physiologic Waveforms
The Learning Electronic Health Record
The Role of Data Science in Closing the Implementation Gap
Designing and Implementing “Living and Breathing Clinical Trials: An Overview and Lessons Learned from the COVID-19 Pandemic
How Electronic Medical Record Integration Can Support More Efficient Critical Care Clinical Trials
Making the Improbable Possible: Generalizing Models Designed for a Syndrome[1]Based, Heterogeneous Patient Landscape
Clinician Trust in Artificial Intelligence: What is Known and How Trust Can Be Facilitated
Implementing Artificial Intelligence: Assessing the Cost and Benefits of Algorithmic Decision-Making in Critical Care
Critical Bias in Critical Care Devices
