A5091812670- Biomedical Text Mining and Ontologies
- Machine Learning in Healthcare
- Chronic Disease Management Strategies
- Topic Modeling
- Health Systems, Economic Evaluations, Quality of Life
- Pharmaceutical Practices and Patient Outcomes
Fraunhofer Institute for Applied Information Technology
2022
University of Bonn
2021
Europe's healthcare systems require enhanced interoperability and digitalization, driving a demand for innovative solutions to process legacy clinical data. This paper presents the results of our project, which aims leverage Large Language Models (LLMs) extract structured information from unstructured reports, focusing on patient history, diagnoses, treatments, other predefined categories. We developed workflow with user interface evaluated LLMs varying sizes through prompting strategies...
According to the European Union, health care sector is still lacking adoption of healthcare standards and interoperable solutions [11] [13]. The EU expects through exchange data (i) a better access for citizens services, (ii) increasing patients' sovereignty empowerment (iii) service provision research innovation, policymaking, regulatory decisions. To address this challenge, we present in paper some further developments our distributed e-health framework [47] [23] [42] [34]. We...
Medical knowledge graphs (KGs) constructed from Electronic Records (EMR) contain abundant information about patients and medical entities. The utilization of KG embedding models on these data has proven to be efficient for different tasks. However, existing do not properly incorporate patient demographics most them ignore the probabilistic features KG. In this paper, we propose DARLING (Demographic Aware pRobabiListic medIcal kNowledge embeddinG), a demographic-aware framework that...