Workflow for health-related and brain data lifecycle
Poor lifestyle leads potentially to chronic diseases and low-grade physical and mental fitness. However, ahead of time, we can measure and analyze multiple aspects of physical and mental health, such as body parameters, health risk factors, degrees of motivation, and the overall willingness to chang...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Digital Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2022.1025086/full |
| _version_ | 1798016046297251840 |
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| author | Petr Brůha Roman Mouček Roman Mouček Jaromír Salamon Vítězslav Vacek |
| author_facet | Petr Brůha Roman Mouček Roman Mouček Jaromír Salamon Vítězslav Vacek |
| author_sort | Petr Brůha |
| collection | DOAJ |
| description | Poor lifestyle leads potentially to chronic diseases and low-grade physical and mental fitness. However, ahead of time, we can measure and analyze multiple aspects of physical and mental health, such as body parameters, health risk factors, degrees of motivation, and the overall willingness to change the current lifestyle. In conjunction with data representing human brain activity, we can obtain and identify human health problems resulting from a long-term lifestyle more precisely and, where appropriate, improve the quality and length of human life. Currently, brain and physical health-related data are not commonly collected and evaluated together. However, doing that is supposed to be an interesting and viable concept, especially when followed by a more detailed definition and description of their whole processing lifecycle. Moreover, when best practices are used to store, annotate, analyze, and evaluate such data collections, the necessary infrastructure development and more intense cooperation among scientific teams and laboratories are facilitated. This approach also improves the reproducibility of experimental work. As a result, large collections of physical and brain health-related data could provide a robust basis for better interpretation of a person’s overall health. This work aims to overview and reflect some best practices used within global communities to ensure the reproducibility of experiments, collected datasets and related workflows. These best practices concern, e.g., data lifecycle models, FAIR principles, and definitions and implementations of terminologies and ontologies. Then, an example of how an automated workflow system could be created to support the collection, annotation, storage, analysis, and publication of findings is shown. The Body in Numbers pilot system, also utilizing software engineering best practices, was developed to implement the concept of such an automated workflow system. It is unique just due to the combination of the processing and evaluation of physical and brain (electrophysiological) data. Its implementation is explored in greater detail, and opportunities to use the gained findings and results throughout various application domains are discussed. |
| first_indexed | 2024-04-11T15:44:27Z |
| format | Article |
| id | doaj.art-2968240bc33d4fea9b261adfa1dc8ddc |
| institution | Directory Open Access Journal |
| issn | 2673-253X |
| language | English |
| last_indexed | 2024-04-11T15:44:27Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj.art-2968240bc33d4fea9b261adfa1dc8ddc2022-12-22T04:15:38ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2022-11-01410.3389/fdgth.2022.10250861025086Workflow for health-related and brain data lifecyclePetr Brůha0Roman Mouček1Roman Mouček2Jaromír Salamon3Vítězslav Vacek4Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech RepublicDepartment of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech RepublicNew Technologies for the Information Society, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech RepublicDepartment of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech RepublicDepartment of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech RepublicPoor lifestyle leads potentially to chronic diseases and low-grade physical and mental fitness. However, ahead of time, we can measure and analyze multiple aspects of physical and mental health, such as body parameters, health risk factors, degrees of motivation, and the overall willingness to change the current lifestyle. In conjunction with data representing human brain activity, we can obtain and identify human health problems resulting from a long-term lifestyle more precisely and, where appropriate, improve the quality and length of human life. Currently, brain and physical health-related data are not commonly collected and evaluated together. However, doing that is supposed to be an interesting and viable concept, especially when followed by a more detailed definition and description of their whole processing lifecycle. Moreover, when best practices are used to store, annotate, analyze, and evaluate such data collections, the necessary infrastructure development and more intense cooperation among scientific teams and laboratories are facilitated. This approach also improves the reproducibility of experimental work. As a result, large collections of physical and brain health-related data could provide a robust basis for better interpretation of a person’s overall health. This work aims to overview and reflect some best practices used within global communities to ensure the reproducibility of experiments, collected datasets and related workflows. These best practices concern, e.g., data lifecycle models, FAIR principles, and definitions and implementations of terminologies and ontologies. Then, an example of how an automated workflow system could be created to support the collection, annotation, storage, analysis, and publication of findings is shown. The Body in Numbers pilot system, also utilizing software engineering best practices, was developed to implement the concept of such an automated workflow system. It is unique just due to the combination of the processing and evaluation of physical and brain (electrophysiological) data. Its implementation is explored in greater detail, and opportunities to use the gained findings and results throughout various application domains are discussed.https://www.frontiersin.org/articles/10.3389/fdgth.2022.1025086/fullbest practicesbrain datadata lifecyclehealth information systemhealth-related dataphysical data |
| spellingShingle | Petr Brůha Roman Mouček Roman Mouček Jaromír Salamon Vítězslav Vacek Workflow for health-related and brain data lifecycle Frontiers in Digital Health best practices brain data data lifecycle health information system health-related data physical data |
| title | Workflow for health-related and brain data lifecycle |
| title_full | Workflow for health-related and brain data lifecycle |
| title_fullStr | Workflow for health-related and brain data lifecycle |
| title_full_unstemmed | Workflow for health-related and brain data lifecycle |
| title_short | Workflow for health-related and brain data lifecycle |
| title_sort | workflow for health related and brain data lifecycle |
| topic | best practices brain data data lifecycle health information system health-related data physical data |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2022.1025086/full |
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