Data Science in Critical Care, An Issue of Critical Care Clinics, 1st Edition
Authors :
Rishikesan Kamaleswaran & Andre L. Holder
Date of Publication: 09/2023
In this issue of Critical Care Clinics, guest editors Drs. Rishikesan Kamaleswaran and Andre L. Holder bring their considerable expertise to the topic of Data Science in Critical Care. Data science, the field of study dedicated to the principled extr ...view more
In this issue of Critical Care Clinics, guest editors Drs. Rishikesan Kamaleswaran and Andre L. Holder bring their considerable expertise to the topic of Data Science in Critical Care. Data science, the field of study dedicated to the principled extraction of knowledge from complex data, is particularly relevant in the critical care setting. In this issue, top experts in the field cover key topics such as refining our understanding and classification of critical illness using biomarker-based phenotyping; predictive modeling using AI/ML on EHR data; classification and prediction using waveform-based data; creating trustworthy and fair AI systems; and more.
In this issue of Critical Care Clinics, guest editors Drs. Rishikesan Kamaleswaran and Andre L. Holder bring their considerable expertise to the topic of Data Science in Critical Care. Data science, the field of study dedicated to the principled extraction of knowledge from complex data, is particularly relevant in the critical care setting. In this issue, top experts in the field cover key topics such as refining our understanding and classification of critical illness using biomarker-based phenotyping; predictive modeling using AI/ML on EHR data; classification and prediction using waveform-based data; creating trustworthy and fair AI systems; and more.
Key Features
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.
Author Information
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
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https://www.uk.elsevierhealth.com/data-science-in-critical-care-an-issue-of-critical-care-clinics-9780443181931.html304167Data Science in Critical Care, An Issue of Critical Care Clinicshttps://www.uk.elsevierhealth.com/media/catalog/product/9/7/9780443181931_6.jpg46.4961.99GBPInStock/Medicine and Surgery/Critical Care/Books/Clinics Review Articles100595255039561283917453100005145120In this issue of Critical Care Clinics, guest editors Drs. Rishikesan Kamaleswaran and Andre L. Holder bring their considerable expertise to the topic of Data Science in Critical Care. Data science, the field of study dedicated to the principled extraction of knowledge from complex data, is particularly relevant in the critical care setting. In this issue, top experts in the field cover key topics such as refining our understanding and classification of critical illness using biomarker-based phenotyping; predictive modeling using AI/ML on EHR data; classification and prediction using waveform-based data; creating trustworthy and fair AI systems; and more. In this issue of Critical Care Clinics, guest editors Drs. Rishikesan Kamaleswaran and Andre L. Holder bring their considerable expertise to the topic of Data Science in Critical Care. Data science, the field of study dedicated to the principled extraction of knowledge from complex data, is particularly relevant in the critical care setting. In this issue, top experts in the field cover key topics such as refining our understanding and classification of critical illness using biomarker-based phenotyping; predictive modeling using AI/ML on EHR data; classification and prediction using waveform-based data; creating trustworthy and fair AI systems; and more.00add-to-cart97804431819312023ProfessionalEdited by Rishikesan Kamaleswaran, PhD and Andre L. Holder, MD, MS20231Book152w x 229h (6.00" x 9.00")Elsevier24014 Sep 2023PRINT ON DEMAND - DELIVERY CAN TAKE UP TO 10 DAYSEdited by <STRONG>Rishikesan Kamaleswaran</STRONG>, 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 <STRONG>Andre L. Holder</STRONG>, MD, MS, Assistant Professor of Medicine, Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University School of Medicine, Grady Memorial HospitalClinicsThe Clinics: Internal MedicineNoNoNoNoPlease SelectPlease SelectPlease Select
