About this Research Topic

Manuscript Submission Deadline 19 October 2022

Over the past 20 years Medicine in general, and Neurology in specific, has become increasingly digitized. Increasingly, Neurology has made a transition from qualitative to quantitative methods. The conversion of neurological data from free text to a computable format has made the application of digital tools to neurological diagnosis and prognosis a reality. This Research Topic in Frontiers in Digital Health will highlight how the digitization of data has revolutionized Neurology. Themes covered will include natural language processing, ontologies, phenotyping, big data, bio-banks, machine learning, graph theory, network analysis, computational models, electronic health records, telemetry, and teleneurology.

A variety of digital tools that have been applied to human diseases with success. This Research Topic will highlight how these digital tools and approaches have enhanced our understanding of neurological disease. Special emphasis will be placed on digital tools applied to neurology including phenotyping, ontology construction, natural language processing, big data approaches, neurological bio-banks, network analysis, computational models, graph theory, teleneurology, telemetry, and electronic health records.

This Research Topic on Digital Insights into Neurology will include the following themes:

- Phenotyping and phenotype repositories: High throughput and computational phenotypes to improve diagnosis and prognosis of neurological disease

- Ontologies: Ontology-based approaches to neurological disease

- Machine Learning: Use of machine learning to enhance neurological diagnosis and prognosis

- Biobanks: Use of disease specific bio-banks to create datasets for neurological diseases

- Natural Language Processing: Using NLP to unlock hidden value in free text, online text databases, and electronic health records

- Network analysis and Graph Theory: Insights into neurological disease based on graph theory and network analysis

- Big Data: Big data, small data, and hybrid approaches to unlocking insights from datasets

- Computational Models: Computational and mathematical models that give insight into neurological disease

- Teleneurology: Improved care for neurological patients via teleneurology

- Telemetry: Improved remote monitoring of neurological conditons via sensors and telemetry

- Electronic health records: Unlocking information in EHRs to improve neurological diagnosis and care

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Dr. Michael Carrithers receives grant support from Biogen Inc. All other Topic Editors declare no conflicts of interest.

Keywords: big data, Neurology, electronic health records, computational neurology, teleneurology, telemetry, network analysis, phenotyping, ontologies, biobanks, natural language processing


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.

Over the past 20 years Medicine in general, and Neurology in specific, has become increasingly digitized. Increasingly, Neurology has made a transition from qualitative to quantitative methods. The conversion of neurological data from free text to a computable format has made the application of digital tools to neurological diagnosis and prognosis a reality. This Research Topic in Frontiers in Digital Health will highlight how the digitization of data has revolutionized Neurology. Themes covered will include natural language processing, ontologies, phenotyping, big data, bio-banks, machine learning, graph theory, network analysis, computational models, electronic health records, telemetry, and teleneurology.

A variety of digital tools that have been applied to human diseases with success. This Research Topic will highlight how these digital tools and approaches have enhanced our understanding of neurological disease. Special emphasis will be placed on digital tools applied to neurology including phenotyping, ontology construction, natural language processing, big data approaches, neurological bio-banks, network analysis, computational models, graph theory, teleneurology, telemetry, and electronic health records.

This Research Topic on Digital Insights into Neurology will include the following themes:

- Phenotyping and phenotype repositories: High throughput and computational phenotypes to improve diagnosis and prognosis of neurological disease

- Ontologies: Ontology-based approaches to neurological disease

- Machine Learning: Use of machine learning to enhance neurological diagnosis and prognosis

- Biobanks: Use of disease specific bio-banks to create datasets for neurological diseases

- Natural Language Processing: Using NLP to unlock hidden value in free text, online text databases, and electronic health records

- Network analysis and Graph Theory: Insights into neurological disease based on graph theory and network analysis

- Big Data: Big data, small data, and hybrid approaches to unlocking insights from datasets

- Computational Models: Computational and mathematical models that give insight into neurological disease

- Teleneurology: Improved care for neurological patients via teleneurology

- Telemetry: Improved remote monitoring of neurological conditons via sensors and telemetry

- Electronic health records: Unlocking information in EHRs to improve neurological diagnosis and care

---
Dr. Michael Carrithers receives grant support from Biogen Inc. All other Topic Editors declare no conflicts of interest.

Keywords: big data, Neurology, electronic health records, computational neurology, teleneurology, telemetry, network analysis, phenotyping, ontologies, biobanks, natural language processing


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.

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