Critical care clinics
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Critical care clinics · Oct 2023
ReviewDesigning and Implementing "Living and Breathing" Clinical Trials: An Overview and Lessons Learned from the COVID-19 Pandemic.
The practice of medicine is characterized by uncertainty, and the findings of randomized clinical trials (RCTs) are meant to help curb that uncertainty. Traditional RCTs, however, have many limitations. ⋯ These new designs recognize uncertainty permeates medical decision making and aim to capitalize on modern health system infrastructure to integrate investigation as a component of care delivery. This article provides an overview of "living, breathing" trials, including current state, anticipated developments, and areas of controversy.
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Critical care clinics · Oct 2023
ReviewImplementing Artificial Intelligence: Assessing the Cost and Benefits of Algorithmic Decision-Making in Critical Care.
This article provides an overview of the most useful artificial intelligence algorithms developed in critical care, followed by a comprehensive outline of the benefits and limitations. We begin by describing how nurses and physicians might be aided by these new technologies. We then move to the possible changes in clinical guidelines with personalized medicine that will allow tailored therapies and probably will increase the quality of the care provided to patients. Finally, we describe how artificial intelligence models can unleash researchers' minds by proposing new strategies, by increasing the quality of clinical practice, and by questioning current knowledge and understanding.
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Critical care clinics · Oct 2023
ReviewPredictive Modeling Using Artificial Intelligence and Machine Learning Algorithms on Electronic Health Record Data: Advantages and Challenges.
The rapid adoption of electronic health record (EHR) systems in US hospitals from 2008 to 2014 produced novel data elements for analysis. Concurrent innovations in computing architecture and machine learning (ML) algorithms have made rapid consumption of health data feasible and a powerful engine for clinical innovation. In critical care research, the net convergence of these trends has resulted in an exponential increase in outcome prediction research. In the following article, we explore the history of outcome prediction in the intensive care unit (ICU), the growing use of EHR data, and the rise of artificial intelligence and ML (AI) in critical care.
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Critical care clinics · Oct 2023
ReviewMachine Learning of Physiologic Waveforms and Electronic Health Record Data: A Large Perioperative Data Set of High-Fidelity Physiologic Waveforms.
Perioperative morbidity and mortality are significantly associated with both static and dynamic perioperative factors. The studies investigating static perioperative factors have been reported; however, there are a limited number of previous studies and data sets analyzing dynamic perioperative factors, including physiologic waveforms, despite its clinical importance. To fill the gap, the authors introduce a novel large size perioperative data set: Machine Learning Of physiologic waveforms and electronic health Record Data (MLORD) data set. They also provide a concise tutorial on machine learning to illustrate predictive models trained on complex and diverse structures in the MLORD data set.
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Critical care clinics · Oct 2023
ReviewMaking the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape.
Syndromic conditions, such as sepsis, are commonly encountered in the intensive care unit. Although these conditions are easy for clinicians to grasp, these conditions may limit the performance of machine-learning algorithms. ⋯ Recent advances in data science, such as transfer learning, conformal prediction, and continual learning, may improve generalizability of machine-learning algorithms in critically ill patients. Randomized trials with these approaches are indicated to demonstrate improvements in patient-centered outcomes at this point.