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.
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.
1. Cover image - p. i 2. Title page - p. i 3. Table of Contents - p. i 4. Copyright - p. ii 5. Contributors - p. iii 6. Forthcoming Issues - p. xi 7. Preface - p. xiii 8. Promise 1: Targeted, personalized bedside care - p. xiv 9. Leveraging Data Science and Novel Technologies to Develop and Implement Precision Medicine Strategies in Critical Care - p. 627 10. Predictive Modeling Using Artificial Intelligence and Machine Learning Algorithms on Electronic Health Record Data: Advantages and Challenges - p. 647 11. Machine Learning of Physiologic Waveforms and Electronic Health Record Data: A Large Perioperative Data Set of High-Fidelity Physiologic Waveforms - p. 675 12. The Learning Electronic Health Record - p. 689 13. Promise 2: Better understanding of critical care deployment and epidemiology - p. 700 14. The Role of Data Science in Closing the Implementation Gap - p. 701 15. Promise 3: More efficient research practices and strategies - p. 716 16. Designing and Implementing “Living and Breathing” Clinical Trials: An Overview and Lessons Learned from the COVID-19 Pandemic - p. 717 17. How Electronic Medical Record Integration Can Support More Efficient Critical Care Clinical Trials - p. 733 18. The challenges of integrating data science into critical care medicine - p. 749 19. Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape - p. 751 20. Clinician Trust in Artificial Intelligence: What is Known and How Trust Can Be Facilitated - p. 769 21. Bioethical dilemmas with data science in critical care - p. 782 22. Implementing Artificial Intelligence: Assessing the Cost and Benefits of Algorithmic Decision-Making in Critical Care - p. 783 23. Critical Bias in Critical Care Devices - p. 795
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.
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.
1. Cover image - p. i 2. Title page - p. i 3. Table of Contents - p. i 4. Copyright - p. ii 5. Contributors - p. iii 6. Forthcoming Issues - p. xi 7. Preface - p. xiii 8. Promise 1: Targeted, personalized bedside care - p. xiv 9. Leveraging Data Science and Novel Technologies to Develop and Implement Precision Medicine Strategies in Critical Care - p. 627 10. Predictive Modeling Using Artificial Intelligence and Machine Learning Algorithms on Electronic Health Record Data: Advantages and Challenges - p. 647 11. Machine Learning of Physiologic Waveforms and Electronic Health Record Data: A Large Perioperative Data Set of High-Fidelity Physiologic Waveforms - p. 675 12. The Learning Electronic Health Record - p. 689 13. Promise 2: Better understanding of critical care deployment and epidemiology - p. 700 14. The Role of Data Science in Closing the Implementation Gap - p. 701 15. Promise 3: More efficient research practices and strategies - p. 716 16. Designing and Implementing “Living and Breathing” Clinical Trials: An Overview and Lessons Learned from the COVID-19 Pandemic - p. 717 17. How Electronic Medical Record Integration Can Support More Efficient Critical Care Clinical Trials - p. 733 18. The challenges of integrating data science into critical care medicine - p. 749 19. Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape - p. 751 20. Clinician Trust in Artificial Intelligence: What is Known and How Trust Can Be Facilitated - p. 769 21. Bioethical dilemmas with data science in critical care - p. 782 22. Implementing Artificial Intelligence: Assessing the Cost and Benefits of Algorithmic Decision-Making in Critical Care - p. 783 23. Critical Bias in Critical Care Devices - p. 795
