FAIR Health Informatics: A Health Informatics Framework for Verifiable and Explainable Data Analysis
The recent COVID-19 pandemic has hit humanity very hard in ways rarely observed before. In this digitally connected world, the health informatics and investigation domains (both public and private) lack a robust framework to enable rapid investigation and cures. Since the data in the healthcare doma...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Healthcare |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9032/11/12/1713 |
| _version_ | 1797594621517234176 |
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| author | Muhammad Hameed Siddiqi Muhammad Idris Madallah Alruwaili |
| author_facet | Muhammad Hameed Siddiqi Muhammad Idris Madallah Alruwaili |
| author_sort | Muhammad Hameed Siddiqi |
| collection | DOAJ |
| description | The recent COVID-19 pandemic has hit humanity very hard in ways rarely observed before. In this digitally connected world, the health informatics and investigation domains (both public and private) lack a robust framework to enable rapid investigation and cures. Since the data in the healthcare domain are highly confidential, any framework in the healthcare domain must work on real data, be verifiable, and support reproducibility for evidence purposes. In this paper, we propose a health informatics framework that supports data acquisition from various sources in real-time, correlates these data from various sources among each other and to the domain-specific terminologies, and supports querying and analyses. Various sources include sensory data from wearable sensors, clinical investigation (for trials and devices) data from private/public agencies, personnel health records, academic publications in the healthcare domain, and semantic information such as clinical ontologies and the Medical Subject Heading ontology. The linking and correlation of various sources include mapping personnel wearable data to health records, clinical oncology terms to clinical trials, and so on. The framework is designed such that the data are Findable, Accessible, Interoperable, and Reusable with proper Identity and Access Mechanisms. This practically means to tracing and linking each step in the data management lifecycle through discovery, ease of access and exchange, and data reuse. We present a practical use case to correlate a variety of aspects of data relating to a certain medical subject heading from the Medical Subject Headings ontology and academic publications with clinical investigation data. The proposed architecture supports streaming data acquisition and servicing and processing changes throughout the lifecycle of the data management. This is necessary in certain events, such as when the status of a certain clinical or other health-related investigation needs to be updated. In such cases, it is required to track and view the outline of those events for the analysis and traceability of the clinical investigation and to define interventions if necessary. |
| first_indexed | 2024-03-11T02:24:55Z |
| format | Article |
| id | doaj.art-ead4505d92044d31985150790f6cec95 |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-11T02:24:55Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-ead4505d92044d31985150790f6cec952023-11-18T10:38:06ZengMDPI AGHealthcare2227-90322023-06-011112171310.3390/healthcare11121713FAIR Health Informatics: A Health Informatics Framework for Verifiable and Explainable Data AnalysisMuhammad Hameed Siddiqi0Muhammad Idris1Madallah Alruwaili2College of Computer and Information Sciences, Jouf University, Sakaka 73211, Saudi ArabiaUniversite Libre de Bruxelles, 1070 Brussels, BelgiumCollege of Computer and Information Sciences, Jouf University, Sakaka 73211, Saudi ArabiaThe recent COVID-19 pandemic has hit humanity very hard in ways rarely observed before. In this digitally connected world, the health informatics and investigation domains (both public and private) lack a robust framework to enable rapid investigation and cures. Since the data in the healthcare domain are highly confidential, any framework in the healthcare domain must work on real data, be verifiable, and support reproducibility for evidence purposes. In this paper, we propose a health informatics framework that supports data acquisition from various sources in real-time, correlates these data from various sources among each other and to the domain-specific terminologies, and supports querying and analyses. Various sources include sensory data from wearable sensors, clinical investigation (for trials and devices) data from private/public agencies, personnel health records, academic publications in the healthcare domain, and semantic information such as clinical ontologies and the Medical Subject Heading ontology. The linking and correlation of various sources include mapping personnel wearable data to health records, clinical oncology terms to clinical trials, and so on. The framework is designed such that the data are Findable, Accessible, Interoperable, and Reusable with proper Identity and Access Mechanisms. This practically means to tracing and linking each step in the data management lifecycle through discovery, ease of access and exchange, and data reuse. We present a practical use case to correlate a variety of aspects of data relating to a certain medical subject heading from the Medical Subject Headings ontology and academic publications with clinical investigation data. The proposed architecture supports streaming data acquisition and servicing and processing changes throughout the lifecycle of the data management. This is necessary in certain events, such as when the status of a certain clinical or other health-related investigation needs to be updated. In such cases, it is required to track and view the outline of those events for the analysis and traceability of the clinical investigation and to define interventions if necessary.https://www.mdpi.com/2227-9032/11/12/1713data correlationdata linkingverifiable datadata analysisexplainable decisionsclinical trials |
| spellingShingle | Muhammad Hameed Siddiqi Muhammad Idris Madallah Alruwaili FAIR Health Informatics: A Health Informatics Framework for Verifiable and Explainable Data Analysis Healthcare data correlation data linking verifiable data data analysis explainable decisions clinical trials |
| title | FAIR Health Informatics: A Health Informatics Framework for Verifiable and Explainable Data Analysis |
| title_full | FAIR Health Informatics: A Health Informatics Framework for Verifiable and Explainable Data Analysis |
| title_fullStr | FAIR Health Informatics: A Health Informatics Framework for Verifiable and Explainable Data Analysis |
| title_full_unstemmed | FAIR Health Informatics: A Health Informatics Framework for Verifiable and Explainable Data Analysis |
| title_short | FAIR Health Informatics: A Health Informatics Framework for Verifiable and Explainable Data Analysis |
| title_sort | fair health informatics a health informatics framework for verifiable and explainable data analysis |
| topic | data correlation data linking verifiable data data analysis explainable decisions clinical trials |
| url | https://www.mdpi.com/2227-9032/11/12/1713 |
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