Semantic Mapping of Named-Entities in openEHR Templates and Ad-hoc Generation of Compositions.

Integration of free texts from reports written by physicians to an interoperable standard is important for improving patient-centric care and research in the medical domain. In the context of unstructured clinical data, NLP Information Extraction serves in finding information in unstructured text. To our best knowledge, there is no efficient solution, in which extracted Named-Entities of an NLP pipeline can be ad-hoc inserted in openEHR compositions.

Programming techniques for improving rule readability for rule-based information extraction natural language processing pipelines of unstructured and semi-structured medical texts.

Background: Extraction of medical terms and their corresponding values from semi-structured and unstructured texts of medical reports can be a time-consuming and error-prone process. Methods of natural language processing (NLP) can help define an extraction pipeline for accomplishing a structured format transformation strategy.

Objectives: In this paper, we build an NLP pipeline to extract values of the classification of malignant tumors (TNM) from unstructured and semi-structured pathology reports and import them further to a structured data source for a clinical study.

Structured, Harmonized, and Interoperable Integration of Clinical Routine Data to Compute Heart Failure Risk Scores.

Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format.

openEHR-to-FHIR: Converting openEHR Compositions to Fast Healthcare Interoperability Resources (FHIR) for the German Corona Consensus Dataset (GECCO).

The German Corona Consensus (GECCO) established a uniform dataset in FHIR format for exchanging and sharing interoperable COVID-19 patient specific data between health information systems (HIS) for universities. For sharing the COVID-19 information with other locations that use openEHR, the data are to be converted in FHIR format. In this paper, we introduce our solution through a web-tool named "openEHR-to-FHIR" that converts compositions from an openEHR repository and stores in their respective GECCO FHIR profiles.

vivaGen - a survival data set generator for software testing.

An amendment to this paper has been published and can be accessed via the original article.