data_harmonization

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Data harmonization

Data harmonization is the improvement of data quality and utilization through the use of machine learning capabilities. Data harmonization interprets existing characteristics of data and action taken on data and uses that information to transform or suggest subsequent data quality improvements.


A significant portion of data in Electronic Health Records is only available as unstructured text, such as surgical or finding reports, clinical notes, and discharge summaries. To use this data for secondary purposes, natural language processing (NLP) tools are required to extract structured information. Furthermore, for interoperable use, data harmonization. HL7 Fast Healthcare Interoperability Resources (FHIR), an emerging standard for exchanging healthcare data, defines such a structured format. For German-language medical NLP, the tool Averbis Health Discovery (AHD) represents a comprehensive solution. AHD offers a proprietary REST interface for text analysis pipelines. To build a bridge between FHIR and this interface, we created a service that translates the communication around AHD from and to FHIR. The application is available under an open-source license 1).


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