Description
The importance of advanced computational modeling for clinical diagnosis is becoming increasingly recognized with the rise of e-health. In particular, given the outbreak of COVID-19, advanced computational models will play a significant role in understanding complex clinical data over the coming years. Computational models have a wide range of applications in clinical settings. For example, advanced computational models can be used to track infectious coronaviruses in populations, identify the most effective interventions, and make predictions regarding disease diagnosis from symptoms, which is critical for saving lives and reducing stress on the healthcare system, especially during infectious disease pandemics. Therefore, advanced computational models can be used to provide precise diagnoses and predictions for clinical decision support.
Although current research in this field has shown promising results, several open research questions remain that need to be addressed through further discussions and studies. Among the many questions include how to learn from high dimensionality when there is only a small amount of labeled biomedical data, how to effectively deal with multi-source and multi-modal biomedical data, how to improve predictive performance along with interpretation, how to share clinical data between hospitals while preserving privacy, and how to build end-to-end disease diagnostic platforms for clinical decision support.
The aim of this Special Issue is to showcase how novel computational methods can be applied to address the challenges of complex biomedical data. Special attention will be devoted to the handling of multi-source and multi-modal biomedical data, limited biomedical data, and multi-institutional biomedical data in terms of privacy preservation. This Special Issue aims to provide stronger technical support and stimulate an environment for the development of computational models and applications for clinical decision support.
Topics of interest
Potential topics include but are not limited to the following:
- Advanced computational models for disease prediction and prevention
- Advanced computational models for small biomedical data
- Advanced computational models for multi-source and multi-modal biomedical data
- Advanced computational models for medical imaging
- Secure biomedical data analysis with advanced computational methods
- Machine learning methods applied to biomedical data
- Advanced computational models for clinical decision support
- End-to-end disease diagnostic platforms using advanced computational models
- Advanced computational models with interpretation
- Advanced computational models for imbalanced biomedical data
- Computer-aided detection and diagnosis
Submission deadline
Deadline for manuscript submissions: 31 December 2022.
Submission URL
https://www.mdpi.com/journal/biology/special_issues/Computational_Clinical
Editors
Dr. Haishuai Wang, Department of Computer Science and Engineering, Fairfield University, Fairfield, CT 06824, USA
Prof. Dr. Chi-Hua Chen, College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China
Dr. Lianhua Chi, Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia
Dr. Jun Wu, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
Dr. Shirui Pan, Department of Data Science & AI, Monash University, Melbourne, VIC 3800, Australia
Dr. Li Li, Department of Genetics, Harvard University, Boston, MA 02115, USA