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Data Science in Critical Care, An Issue of Critical Care Clinics

Data Science in Critical Care, An Issue of Critical Care Clinics

9780443181931
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Description
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.
Product Details
Elsevier
98513
9780443181931
9780443181931

Data sheet

Publication date
2023
Issue number
1
Cover
hard cover
Pages count
240
Dimensions (mm)
152 x 229
Weight (g)
480
  • Leveraging Data Science and Novel Technologies to Develop and Implement Precision Medicine Strategies in Critical Care
    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
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