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

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. INDICE: Leveraging Data Science and Novel Technologies to Develop and Implement Precision Medicine Strategies in Critical CarePredictive Modeling Using Artificial Intelligence and Machine Learning Algorithms on Electronic Health Record Data: Advantages and ChallengesMachine Learning of Physiologic Waveforms and Electronic Health Record Data: A Large Perioperative Data Set of High-Fidelity Physiologic WaveformsThe Learning Electronic Health RecordThe Role of Data Science in Closing the Implementation GapDesigning and Implementing Living and Breathing? Clinical Trials: An Overview and Lessons Learned from the COVID-19 PandemicHow Electronic Medical Record Integration Can Support More Efficient Critical Care Clinical TrialsMaking the Improbable Possible: Generalizing Models Designed for a Syndrome[1]Based, Heterogeneous Patient LandscapeClinician Trust in Artificial Intelligence: What is Known and How Trust Can Be FacilitatedImplementing Artificial Intelligence: Assessing the Cost and Benefits of Algorithmic Decision-Making in Critical CareCritical Bias in Critical Care Devices

• ISBN: 978-0-443-18193-1
• Editorial: Elsevier
• Encuadernacion: Cartoné
• Páginas: 240
• Fecha Publicación: 14/09/2023
• Nº Volúmenes: 1
• Idioma: Inglés