In recent years, the development of biomedical imaging techniques, integrative sensors, and artificial intelligence has brought many benefits to the protection of health. We can collect, measure, and analyze vast volumes of health-related data using the technologies of computing and networking, leading to tremendous opportunities for the health and biomedical community. Biomedical intelligence, especially precision medicine, is considered one of the most promising directions for healthcare development. Meanwhile, these technologies have also brought new challenges and issues.
The statistical analysis and presentation of data are essential to the practice of biomedical intelligence. Biomedical computing systems need to statistically analyze multi-modal biomedical data, such as genetic data, biomedical data, and data collected from mobile healthcare devices. Afterward, informatics is essential for physicians to understand the characteristics of such data and discover connections to human health. Furthermore, since genetic sequencing is critical for precision medicine, it is important to conduct prescriptive and predictive analytics based on genetic sequencing data. Finally, mobile health is a current hot topic and has led to many inspiring results. Efficient collection, visualization, analysis, and mining of mobile health data should be further explored. Machine learning plays an important role in the multi-modal biomedical data processing. It is expected that the efficiency, accuracy, predictive value, and benefits of biomedical intelligence computing will greatly improve in the years to come.
The aims of this Research Topic are 1) to present the state-of-the-art research on machine learning used in biomedical computing and intelligence healthcare, and 2) to provide a forum for experts to disseminate their recent advances and views on future perspectives in the field. Researchers from academic fields and industries worldwide are encouraged to submit high quality unpublished Original Research articles as well as Review articles in broad areas relevant to machine learning theories and technologies for biomedical intelligence engineering.
Authors are invited to submit a manuscript. Relevant topics of interest to this special section include (but are not limited to):
• Informatics of multi-modal biomedical data, such as genetic data, biomedical data, and data collected from mobile healthcare devices.
• Prescriptive and predictive analytics based on genetic sequencing data.
• Collection, visualization, analysis, and mining of data about mobile health.
• Machine learning-based processing and diagnostic analysis of biomedical data, such as nodule detection in CT images, enhancement of low-quality images, etc.
• Intelligent interrogation systems, such as health-related dialogue agents.
• Construction, analysis, and use of health-related knowledge graph.
• Adversarial training on biomedical images and other health data.
• Visualization and understanding of machine learning in biomedical engineering.
• Curative effect evaluation and prediction based on machine learning techniques.
• Hardware or database architectures that can implicitly capture intricate structures of large-scale multi-modal biomedical data.
• Improvising on the computation of biomedical processing models, exploiting parallel computation techniques, and GPU programming.
• Cloud, fog, and edge computing systems for biomedical data processing, analysis, etc.
• Security, privacy, and trust in biomedical intelligence systems.
In recent years, the development of biomedical imaging techniques, integrative sensors, and artificial intelligence has brought many benefits to the protection of health. We can collect, measure, and analyze vast volumes of health-related data using the technologies of computing and networking, leading to tremendous opportunities for the health and biomedical community. Biomedical intelligence, especially precision medicine, is considered one of the most promising directions for healthcare development. Meanwhile, these technologies have also brought new challenges and issues.
The statistical analysis and presentation of data are essential to the practice of biomedical intelligence. Biomedical computing systems need to statistically analyze multi-modal biomedical data, such as genetic data, biomedical data, and data collected from mobile healthcare devices. Afterward, informatics is essential for physicians to understand the characteristics of such data and discover connections to human health. Furthermore, since genetic sequencing is critical for precision medicine, it is important to conduct prescriptive and predictive analytics based on genetic sequencing data. Finally, mobile health is a current hot topic and has led to many inspiring results. Efficient collection, visualization, analysis, and mining of mobile health data should be further explored. Machine learning plays an important role in the multi-modal biomedical data processing. It is expected that the efficiency, accuracy, predictive value, and benefits of biomedical intelligence computing will greatly improve in the years to come.
The aims of this Research Topic are 1) to present the state-of-the-art research on machine learning used in biomedical computing and intelligence healthcare, and 2) to provide a forum for experts to disseminate their recent advances and views on future perspectives in the field. Researchers from academic fields and industries worldwide are encouraged to submit high quality unpublished Original Research articles as well as Review articles in broad areas relevant to machine learning theories and technologies for biomedical intelligence engineering.
Authors are invited to submit a manuscript. Relevant topics of interest to this special section include (but are not limited to):
• Informatics of multi-modal biomedical data, such as genetic data, biomedical data, and data collected from mobile healthcare devices.
• Prescriptive and predictive analytics based on genetic sequencing data.
• Collection, visualization, analysis, and mining of data about mobile health.
• Machine learning-based processing and diagnostic analysis of biomedical data, such as nodule detection in CT images, enhancement of low-quality images, etc.
• Intelligent interrogation systems, such as health-related dialogue agents.
• Construction, analysis, and use of health-related knowledge graph.
• Adversarial training on biomedical images and other health data.
• Visualization and understanding of machine learning in biomedical engineering.
• Curative effect evaluation and prediction based on machine learning techniques.
• Hardware or database architectures that can implicitly capture intricate structures of large-scale multi-modal biomedical data.
• Improvising on the computation of biomedical processing models, exploiting parallel computation techniques, and GPU programming.
• Cloud, fog, and edge computing systems for biomedical data processing, analysis, etc.
• Security, privacy, and trust in biomedical intelligence systems.