Journal:FAIR Health Informatics: A health informatics framework for verifiable and explainable data analysis
| Full article title | FAIR Health Informatics: A health informatics framework for verifiable and explainable data analysis |
|---|---|
| Journal | Healthcare |
| Author(s) | Siddiqi, Muhammad H.; Idris, Muhammad; Alruwaili, Madallah |
| Author affiliation(s) | Jouf University, Universite Libre de Bruxelles |
| Primary contact | Email: mhsiddiqi at ju dot edu dot sa |
| Year published | 2023 |
| Volume and issue | 11(12) |
| Article # | 1713 |
| DOI | 10.3390/healthcare11121713 |
| ISSN | 2227-9032 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://www.mdpi.com/2227-9032/11/12/1713 |
| Download | https://www.mdpi.com/2227-9032/11/12/1713/pdf?version=1686475395 (PDF) |
Abstract
The recent COVID-19 pandemic has hit humanity very hard in ways rarely observed before. In this digitally connected world, the health informatics and clinical research domains (both public and private) lack a robust framework to enable rapid investigation and cures. Since 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, personal health records, academic publications in the healthcare domain, and semantic information such as clinical ontologies and the Medical Subject Headings (MeSH) ontology. The linking and correlation of various sources include mapping personal 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 (FAIR) with proper identity and access management mechanisms. This practically means 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 MeSH 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 process. 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.
Keywords: data correlation, data linking, verifiable data, data analysis, explainable decisions, clinical trials, COVID, clinical investigation, semantic mapping, smart health
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This presentation is faithful to the original, with only a few minor changes to presentation, though grammar and word usage was substantially updated for improved readability. In some cases important information was missing from the references, and that information was added.
