This Research Topic is the fifth volume of the series Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine
Volume I:
Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume IVolume II:
Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume IIVolume III:
Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume IIIVolume IV:
Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume IVAnalytics based on artificial intelligence has greatly advanced scientific research fields like natural language processing and imaging classification. Clinical research has also greatly benefited from artificial intelligence. Emergency and critical care physicians face patients with rapidly changing conditions, which require accurate risk stratification and initiation of rescue therapy. Furthermore, critically ill patients, such as those with sepsis, acute respiratory distress syndrome, and trauma, are comprised of heterogeneous population. The “one-size-fit-all” paradigm may not fit for the management of such heterogeneous patient population. Thus, artificial intelligence can be employed to identify novel subphenotypes of these patients. These sub classifications can provide not only prognostic value for risk stratification but also predictive value for individualized treatment. With the development of transcriptome providing a large amount of information for an individual, artificial intelligence can greatly help to identify useful information from high dimensional data. Altogether, it is of great importance to further utilize artificial intelligence in the management of critically ill patients.
The Research Topic primarily focuses on the use of artificial intelligence in the diagnosis and treatment of patients in emergency or critical care settings. In particular, a large amount of data are being generated from electronic healthcare records and transcriptome analysis. Novel methods from artificial intelligence can help to address the curse of dimensionality as have been frequently encountered when a large number of variables are being processed with conventional methods. This Research Topic also welcomes submissions of bioinformatic analysis with methods such as deep learning, density estimation, and reinforcement learning. In such a way, these advanced machine learning methods can help to provide novel findings from large dataset, comparing with traditional methods in the context of epidemiology and medical statistics which may fail to provide such novel findings due to their intrinsic limitations.
We welcome submissions of Original Research, Review, and Opinions. The subject areas of interest include but are not limited to:
• Predictive analytics for risk stratification of emergency and critically ill patients
• Individualized treatment strategy for patients with rapidly changing conditions
• Sub-phenotypes of heterogenous population in emergency and critical care setting
• Bioinformatics analysis with transcriptome to develop individualized management
This Research Topic is the fifth volume of the series Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine
Volume I:
Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume IVolume II:
Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume IIVolume III:
Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume IIIVolume IV:
Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume IVAnalytics based on artificial intelligence has greatly advanced scientific research fields like natural language processing and imaging classification. Clinical research has also greatly benefited from artificial intelligence. Emergency and critical care physicians face patients with rapidly changing conditions, which require accurate risk stratification and initiation of rescue therapy. Furthermore, critically ill patients, such as those with sepsis, acute respiratory distress syndrome, and trauma, are comprised of heterogeneous population. The “one-size-fit-all” paradigm may not fit for the management of such heterogeneous patient population. Thus, artificial intelligence can be employed to identify novel subphenotypes of these patients. These sub classifications can provide not only prognostic value for risk stratification but also predictive value for individualized treatment. With the development of transcriptome providing a large amount of information for an individual, artificial intelligence can greatly help to identify useful information from high dimensional data. Altogether, it is of great importance to further utilize artificial intelligence in the management of critically ill patients.
The Research Topic primarily focuses on the use of artificial intelligence in the diagnosis and treatment of patients in emergency or critical care settings. In particular, a large amount of data are being generated from electronic healthcare records and transcriptome analysis. Novel methods from artificial intelligence can help to address the curse of dimensionality as have been frequently encountered when a large number of variables are being processed with conventional methods. This Research Topic also welcomes submissions of bioinformatic analysis with methods such as deep learning, density estimation, and reinforcement learning. In such a way, these advanced machine learning methods can help to provide novel findings from large dataset, comparing with traditional methods in the context of epidemiology and medical statistics which may fail to provide such novel findings due to their intrinsic limitations.
We welcome submissions of Original Research, Review, and Opinions. The subject areas of interest include but are not limited to:
• Predictive analytics for risk stratification of emergency and critically ill patients
• Individualized treatment strategy for patients with rapidly changing conditions
• Sub-phenotypes of heterogenous population in emergency and critical care setting
• Bioinformatics analysis with transcriptome to develop individualized management
