About this Research Topic
The amount of data produced within medical and health Informatics has grown to be quite vast, and analysis of this Big Data grants potentially limitless possibilities for knowledge to be gained. This progress is being spurred by the development of Electronic Health Records (EHR), health insurance claims, medical imaging databases, disease registries, spontaneous reporting sites, clinical trials, as well as user-generated contents from social media and wearable devices. As a result, mining large-scale data in medical and health informatics has become critical to the healthcare world for deriving phenotypic and genotypic signatures of complex illnesses from depression to cancer, and has begun to translate them to guide personalized clinical decisions.
The scale and complexity of medical and health informatics data provide unprecedented opportunities in enhancing mechanistic understanding of complex diseases, which can benefit public health outcomes by facilitating diagnostic and therapeutic progress. However, due to the high dimensionality and complex structure of these data sets, this field faces major computational challenges.
The goal of this Research Topic is to present the latest research regarding reliable innovative solutions that are applied to healthcare to enhance the quality of life, as well as related issues and challenges. Contributions to illustrate both the development of new medical and health informatics resources (including databases and tools) and the application of big data analytic approaches for assisting medical image computing and computer-assisted interventions to understand and treat rare and complex diseases are welcomed.
This Research Topic seeks research in, but is not limited to, integrative analysis of multidimensional and large-scale medical and health data for exploring their connections to provide important new insights into the phenotypic characteristics and genetic mechanisms of normal and/or disordered biological structures and functions of rarely or commonly seen diseases, such as neurodegenerative disorder, cancers, blood diseases, to name a few. We are particularly interested in new and emerging big data mining methods development and analyses across multiple data types that inform precision medicine discovery or implementation efforts including genotype-phenotype data, other 'omic’ data, lifestyle and environmental variables, and electronic health records data.
Possible topics may include:
• Recent advancements of machine learning and/or data mining methods to facilitate medical informatics and health data analytics
• Knowledge representation and reasoning for medical data
• Statistical analysis and characterization of health data
• Deep learning enabled methods for mining free text in electronic medical records
• Innovative use of health data for improved understanding of patient care and treatment outcomes with reduced costs
• Pattern detection and hypothesis generation data
• Methods for quantifying and exploring multidimensional individual genetic and phenotypic vulnerability as well as electronic health records
• Novel methods to handle medical data across cohorts and modalities, and integrate multi-cohort data
• Distributed machine learning methods for integrating private multi-site medical imaging data
• Federated learning-based methods for improved privacy and efficiency in medical and health informatics
• Biomedical imaging devices assisted by Artificial Intelligence in the health domain
The scale and complexity of medical and health informatics data provide unprecedented opportunities in enhancing mechanistic understanding of complex diseases, which can benefit public health outcomes by facilitating diagnostic and therapeutic progress. However, due to the high dimensionality and complex structure of these data sets, this field faces major computational challenges.
The goal of this Research Topic is to present the latest research regarding reliable innovative solutions that are applied to healthcare to enhance the quality of life, as well as related issues and challenges. Contributions to illustrate both the development of new medical and health informatics resources (including databases and tools) and the application of big data analytic approaches for assisting medical image computing and computer-assisted interventions to understand and treat rare and complex diseases are welcomed.
This Research Topic seeks research in, but is not limited to, integrative analysis of multidimensional and large-scale medical and health data for exploring their connections to provide important new insights into the phenotypic characteristics and genetic mechanisms of normal and/or disordered biological structures and functions of rarely or commonly seen diseases, such as neurodegenerative disorder, cancers, blood diseases, to name a few. We are particularly interested in new and emerging big data mining methods development and analyses across multiple data types that inform precision medicine discovery or implementation efforts including genotype-phenotype data, other 'omic’ data, lifestyle and environmental variables, and electronic health records data.
Possible topics may include:
• Recent advancements of machine learning and/or data mining methods to facilitate medical informatics and health data analytics
• Knowledge representation and reasoning for medical data
• Statistical analysis and characterization of health data
• Deep learning enabled methods for mining free text in electronic medical records
• Innovative use of health data for improved understanding of patient care and treatment outcomes with reduced costs
• Pattern detection and hypothesis generation data
• Methods for quantifying and exploring multidimensional individual genetic and phenotypic vulnerability as well as electronic health records
• Novel methods to handle medical data across cohorts and modalities, and integrate multi-cohort data
• Distributed machine learning methods for integrating private multi-site medical imaging data
• Federated learning-based methods for improved privacy and efficiency in medical and health informatics
• Biomedical imaging devices assisted by Artificial Intelligence in the health domain
Keywords: Big Data, Data Mining, Machine Learning, Artificial Intelligence, Clinical Decision Support Systems, Knowledge Extraction, Healthcare
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
