Advances in medical sensors and data storage technologies have enabled the cumulation of a large amount of data in various dimensions in neurology. Data intelligence integrates data from various sources, applies advanced analytical tools and techniques, and presents the results in a way that is easily understood by decision-makers. Both structural and non-structural data in neurology are collected, which covers data formats of time-series, text, images, sound, et al. Since data can systematically describe the condition of patients, their utilization has been attending growing interest in clinical practice and data intelligence methods are highly desired. Meanwhile, the theoretical development in related disciplines, such as machine learning, computer vision, evolutionary computation, and signal processing, has provided effective ways of analyzing and utilizing the collected data. The recent success of applying data-driven methods in diagnosis and therapeutic interventions of neurological diseases, including early neurological deterioration detection, stroke lesion segmentation and stroke risk prediction, has proved the potential of employing data intelligence algorithms in neurology.
This research topic aims to provide a forum for researchers to present the latest research on clinical applications of data intelligence algorithms in neurology, especially considering data fusion approaches of integrating different data sources and fusing structural and non-structural information.
The list of possible topics includes, but is not limited to:
- Brain Images Analysis and Diagnostics Using Data-Driven Computer Vision Algorithms
- Data Analytics and Mining for Neurological Disease Diagnostics
- Data-Driven Predictive Models for Acute Stroke Management
- Data Fusion Techniques in Acute Neurology
- Data-Driven neuroimaging for Rapid Diagnosis
- Data Intelligence in Neurocritical Care and Telemedicine
Advances in medical sensors and data storage technologies have enabled the cumulation of a large amount of data in various dimensions in neurology. Data intelligence integrates data from various sources, applies advanced analytical tools and techniques, and presents the results in a way that is easily understood by decision-makers. Both structural and non-structural data in neurology are collected, which covers data formats of time-series, text, images, sound, et al. Since data can systematically describe the condition of patients, their utilization has been attending growing interest in clinical practice and data intelligence methods are highly desired. Meanwhile, the theoretical development in related disciplines, such as machine learning, computer vision, evolutionary computation, and signal processing, has provided effective ways of analyzing and utilizing the collected data. The recent success of applying data-driven methods in diagnosis and therapeutic interventions of neurological diseases, including early neurological deterioration detection, stroke lesion segmentation and stroke risk prediction, has proved the potential of employing data intelligence algorithms in neurology.
This research topic aims to provide a forum for researchers to present the latest research on clinical applications of data intelligence algorithms in neurology, especially considering data fusion approaches of integrating different data sources and fusing structural and non-structural information.
The list of possible topics includes, but is not limited to:
- Brain Images Analysis and Diagnostics Using Data-Driven Computer Vision Algorithms
- Data Analytics and Mining for Neurological Disease Diagnostics
- Data-Driven Predictive Models for Acute Stroke Management
- Data Fusion Techniques in Acute Neurology
- Data-Driven neuroimaging for Rapid Diagnosis
- Data Intelligence in Neurocritical Care and Telemedicine
