Aims & Objectives
There is a scarcity or often even absence to have patient reported outcomes data available in a structured fashion. Where data is digitally recorded they often reside in well-protected silos. Several processes still rely on manual and paper-based processes. Additionally, patients are plagued with repeating even basics such as giving their medical history from provider to provider.
LeMeDaRT aims to address these issues by building a ”liquid data” concept to link and digitally support the patient journey from population health over prevention to interaction of rural care including complex interventions. The core goal is to provide the right relevant data in each instance during the patient journey to leverage patient benefit and provider efficiency.
Role of the EMCL
The consortium is composed of 5 Cluster–Groups. The EMCL is part of the Cluster-Group 1 which is responsible for the Data Integration & Technology as well as Data Science. More specifically, the cluster is part of 4 out of 22 work packages including:
- WP 3: Enrichment of existing data sources by clinical information.
- WP 4: Establishing of vertical data integration of preventive services.
- WP 6: Visualization of data for specific end-users, usability.
- WP 12: Application of machine learning to use cases.
The task is to develop data visualizations for end-users in the digital hub (WP 6) from data in the system (WP 4), enriched by external or currently available additional information on clinical courses (WP 3) to provide a significant improvement for both decision makers and individuals. From a mathematical point of view (WP 12), these procedures use Bayes' theorem, translating this into comprehensible information (WP 6). At a later stage these algorithms can be extended by neural networks and other forms of machine learning (WP12).
In practice, these methods will be applied to three use cases:
- Prehabilitation and enhanced postsurgical surveillance in complex abdominal cancer surgery
- Infection surveillance in respiratory viral disease
- Prevention and early intervention clinically silent liver disease
- Uncertainty quantification of neural networks and bayesian models
- Data assimiliation for a standardized clinical data format
- Causal Inference and patient classification using neural networks
- Translation of the innovative developments to clinical practice
- The project is founded by Bundesministerium für Bildung und Forschung - (BMBF).
People from the EMCL