Application of Artificial Intelligence to Advance Individualized Diagnosis and Treatment in Emergency and Critical Care Medicine—2nd Edition

Dear Colleagues,

Analytics based on artificial intelligence have greatly advanced scientific research fields such as natural language processing and imaging classification. Clinical research has also been greatly influenced by artificial intelligence for diagnostics involving imaging and molecular markers. Emergency and critical care medicine faces patients with rapidly changing conditions, which require the accurate risk stratification and initiation of rescuing therapies. The key to the successful treatment of critically ill patients involves all aspects of diagnostics such as the early prediction of adverse events, correct diagnosis of causative agents and a differential diagnosis for a manifestation. Critically ill patients usually generate a large amount of data from medical devices such as bedside monitors, ventilators and renal replacement therapy devices. Such large volumes of data are difficult for humans to handle. Artificial intelligence can learn complex data structures to obtain knowledge and wisdom and, thus, can have profound impacts on the management of critically ill patients. Furthermore, critically ill patients such as those with sepsis, acute respiratory distress syndrome and trauma comprise a heterogenous population. The “one-size-fit-all” paradigm may not be suitable for the management of such a heterogeneous patient population. Thus, tools from artificial intelligence can be employed to identify novel subphenotypes of these patients. These subclassifications can not only provide prognostic value for risk stratification but also have predictive value for individualized treatment. Transcriptomes can also provide large amounts of information for the patients; artificial intelligence can greatly help us to obtain useful information from such highly dimensional data.

This Special Issue welcomes articles addressing, but not limited to, the following specific topics:

    Predictive analytics for the risk stratification of emergency and critically ill patients.

    Diagnostics for critical syndromes such as sepsis, acute respiratory failure and acute circulatory failure.

    Biomarkers for the differentiation of critical syndromes.

    Individualized treatment strategies for patients with rapidly changing conditions.

    Subphenotypes of heterogenous populations in emergency and critical care settings.

    Bioinformatics analysis with transcriptomes to develop individualized management.

Reviews, original articles and secondary analyses are welcome.

Dr. Zhongheng Zhang
Guest Editor

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• critical care
• sepsis
• artificial intelligence
• predictive analytics
• clustering
• acute kidney injury
• emergency medicine
• transcriptome