The ever-increasing volume of neuroscience data, such as fMRI, MEG, EEG, and more, allows us to gain more insight into discovering neuronal working mechanisms of disease. The interdisciplinary technologies in neuroscience and computational medicine can effectively improve the traditional symptom-driven practice of medicine by accounting for individual variability, behavior, and physiological reactions. These technologies facilitate us to provide earlier interventions and tailor better and economically personalized treatments, especially for neurodegenerative and neurological diseases. However, due to the complexities of disease pathogenesis and neuron activity at the individual level, establishing a precise, generalizable, and robust interpretability healthcare system still faces many challenges. Artificial intelligence technologies in the field of medicine and neuroscience give a compelling vision that has the potential to make improvements for achieving these goals at lower costs.
With the increment of practical large-scale labeled datasets and the enhancement of computing power, many existing constraints for learning complex medicine and neuroscience data have been greatly minimized. However, traditional methods rely on expert systems and utilize known facts and knowledge to make inferences, ignoring the potential associations among the biomarkers, disease progression, neuron activity, and drugs. These associations include disease complications, pharmaceutical composition, drug-disease response, similar symptoms or neuronal activity, and many others. The phenomenon inevitably limits the performance of the clinical analysis and makes it hard to utilize healthcare information in clinical decision-making.
The Research topic aims to comprehensively gather recent advanced interdisciplinary technologies in the aspects of neuroscience, computational medicine, and intelligent aided diagnosis. We welcome Original Articles, Reviews, Comments, and Opinions covering the following specific topics of interest but are not limited to:
- Innovative data fusion and analysis approach for computational medicine or neuroscience in healthcare.
- Advanced machine learning and deep learning approaches related to diagnosis, and healthcare, such as computer-aided diagnosis, and disease corresponding complications prediction.
- Multi-task intelligence method for interdisciplinary application of medicine and neuroscience data.
- Multi-dimensional feature learning for the neural signal in clinic data.
- Interpretable advanced methods for neuroscience data analysis.
The ever-increasing volume of neuroscience data, such as fMRI, MEG, EEG, and more, allows us to gain more insight into discovering neuronal working mechanisms of disease. The interdisciplinary technologies in neuroscience and computational medicine can effectively improve the traditional symptom-driven practice of medicine by accounting for individual variability, behavior, and physiological reactions. These technologies facilitate us to provide earlier interventions and tailor better and economically personalized treatments, especially for neurodegenerative and neurological diseases. However, due to the complexities of disease pathogenesis and neuron activity at the individual level, establishing a precise, generalizable, and robust interpretability healthcare system still faces many challenges. Artificial intelligence technologies in the field of medicine and neuroscience give a compelling vision that has the potential to make improvements for achieving these goals at lower costs.
With the increment of practical large-scale labeled datasets and the enhancement of computing power, many existing constraints for learning complex medicine and neuroscience data have been greatly minimized. However, traditional methods rely on expert systems and utilize known facts and knowledge to make inferences, ignoring the potential associations among the biomarkers, disease progression, neuron activity, and drugs. These associations include disease complications, pharmaceutical composition, drug-disease response, similar symptoms or neuronal activity, and many others. The phenomenon inevitably limits the performance of the clinical analysis and makes it hard to utilize healthcare information in clinical decision-making.
The Research topic aims to comprehensively gather recent advanced interdisciplinary technologies in the aspects of neuroscience, computational medicine, and intelligent aided diagnosis. We welcome Original Articles, Reviews, Comments, and Opinions covering the following specific topics of interest but are not limited to:
- Innovative data fusion and analysis approach for computational medicine or neuroscience in healthcare.
- Advanced machine learning and deep learning approaches related to diagnosis, and healthcare, such as computer-aided diagnosis, and disease corresponding complications prediction.
- Multi-task intelligence method for interdisciplinary application of medicine and neuroscience data.
- Multi-dimensional feature learning for the neural signal in clinic data.
- Interpretable advanced methods for neuroscience data analysis.
