Intensive Care Research - Call for Papers for the Special Issue: Artificial Intelligence in the Emergency and Critical Care Medicine
Guest Editor:
Prof. Zhongheng Zhang
Prof. Chun-An Chou
Aims and scope
Analytics based on artificial intelligence has greatly advanced scientific research fields like natural language processing and imaging classification. Clinical research has also been influenced by the artificial intelligence. Emergency and critical care medicine faces patients with rapidly changing conditions, which require accurate risk stratification and initiation of rescuing therapy. Thus, artificial intelligence can have profound impact on the management of these critically ill patients. Furthermore, critically ill patients such as those with sepsis, acute respiratory distress syndrome and trauma actually are comprised of heterogeneous population. The “one-size-fit-all” paradigm may not fit for the management of such 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 predictive value for individualized treatment. Transcriptome can provide large amount of information for a patient and artificial intelligence can greatly help to identify useful information from such a high dimensional data.
Main topics and quality control
The issue primarily focuses on the use of artificial intelligence for the diagnosis and treatment of patients in emergency or critical care settings. In particular, large amount of data are being generated from electronic healthcare record and transcriptome analysis. Novel methods from artificial intelligence can help to address the curse of dimensionality as have been frequently encountered when large number of variables are being processed with conventional methods. This focused issue also welcomes submissions of bioinformatics 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 amount of data. Traditional methods in the context of epidemiology and medical statistics may fail to provide such novel findings due to their intrinsic limitations. Reviews, opinions, original articles and secondary analysis are welcome.
- Predictive analytics for risk stratification of emergency and critically ill patients
- Individualized treatment strategy for patients with rapidly changing conditions
- Subphenotypes of heterogeneous population in emergency and critical care setting
- Bioinformatics analysis with transcriptome to develop individualized management
Full papers will be subject to a strict review procedure for final selection to this special issue based on the following criteria:
- Quality and originality in theory and methodology of the special issue.
- Relevance to the topic of the special issue.
- Application orientation which exhibits novelty.
- If there is an implementation, the details of the implementation must be provided.
- Presence of the following statements (if applicable): disclosure of potential conflicts of interest, research involving human participants and/or animals, informed consent.
Important date
Open date: 28 Feb. 2022
Submit your paper
All papers have to be submitted via the Editorial Manager online submission and peer review system. Instructions will be provided on screen and you will be stepwise guided through the process of uploading all the relevant article details and files associated with your submission. During submission authors should indicate that their manuscript belongs to the special issue “AI in the Emergency and Critical Care Medicine” (this question will appear at “Additional Information” step). All manuscripts must be submitted in English.
To access the online submission site for the journal, please visit https://www.editorialmanager.com/icrs/default1.aspx (this opens in a new tab) Note that if this is the first time that you submit to the Intensive Care Research, you need to register as a user of the system first.
NOTE : Before submitting your paper, please make sure to review the journal's Author Guideline (this opens in a new tab) first.
After acceptance
This special issue will be published as a virtual collection that will be accessible at SpringerLink.
Accepted papers will be published online within about 20 days after acceptance, fully citable by DOI (Digital Object Identifier), prior to publication in the issue.
Introduction of the Guest Editor
[Zhongheng Zhang] (this opens in a new tab)
Dr. Zhongheng Zhang is a physician of Department of emergency medicine, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China. His major research interests include critical care nephrology, hemodynamics and meta-analysis, delirium in ICU, and outcome study for critically ill patients. He is currently working on the secondary analysis of electronic health records and data mining. As the first author, he has published more than 45 academic papers (science citation indexed) that have been cited for over 5000 times. He has reviewed academic papers for over 40 journals, including the lancet respiratory medicine, Critical Care and journal of clinical epidemiology.
[Chun-An Chou]
Dr. Chun-An Chou is an Assistant Professor in the Department of Mechanical and Industrial Engineering at Northeastern University, Boston, USA. He is currently the vice president of INFORMS Data Mining Society. Prior to the current position, he held faculty positions in the Department of Systems Science and Industrial Engineering and the Center of Collective Dynamics of Complex Systems at Binghamton University, the State University of New York, and worked as a Post-Doctoral Research Associate in the Integrated Brain Imaging Center at University of Washington Medical Center. He received his PhD degree at Rutgers University and MS degree at Columbia University. His research is focused on the development of quantitative decision tools using optimization, data mining, and machine learning techniques to discover and support scientific findings for large-scale complex problems in applications, e.g., medical diagnosis, healthcare operations, and computational neuroscience. He has published more than 80 peer-reviewed academic papers in operations research, healthcare engineering, and biomedical informatics areas.