Projects
CRC 1597 "Small Data" Project A02: Identifying best practice treatment strategies by incorporating information from similar healthcare pathways
Funded by German Research Foundation (DFG)
Funding period: 2023-2027
We assume that routine clinical care data can be used to identify similar treatment pathways even in small-data settings, including situations in which multiple distinct pathways exist and the number of observed transitions per pathway decreases over time. Starting from the treatment trajectories of a small, well-defined cohort of patients, we develop methods to quantify the similarity of other patients’ trajectories to this reference group using multistate models. We assess the uncertainty of these similarity measures, derive confidence intervals, and formulate corresponding hypotheses and statistical tests that enable informed decisions about the similarity between two treatment pathways. In addition, we develop new clustering methods for collections of multistate models to identify groups of patients that represent exemplary or optimal treatment pathways.
More information: https://www.smalldata-initiative.de/projects/a02/
CRC 1597 "Small Data" Integrated Research Training Group (IRTG) SMART
Funded by German Research Foundation (DFG)
Funding period: 2023-2027
The IRTG SMART aims to provide doctoral researchers in CRC 1597 Small Data with a broad interdisciplinary foundation necessary to address methodological small data challenges. A central objective is to foster a shared language that enables researchers from different data-driven disciplines to collaborate effectively across disciplinary boundaries.
More information: https://www.smalldata-initiative.de/career/doctoral-program-smart/
CALM-QE: COPD and Asthma: longitudinal and cross-sector real world data regarding machine learning applications for quality improvement and knowledge gathering
Funded by Federal Ministry of Research, Technology and Space (BMFTR)
Funding period: 2023-2027
CALM-QE is a project within the Medical Informatics Initiative (MII). Its aim is to make healthcare data related to chronic obstructive pulmonary disease (COPD) and bronchial asthma available for research while complying with data protection regulations for highly sensitive personal data. Using these data, the project seeks to develop individualized diagnostic and therapeutic recommendations for patients and to identify personal risk factors that can be addressed through targeted interventions. By applying machine learning methods to these newly accessible, multidimensional, cross-sector datasets, the project aims to identify potentially complex patterns that not only support more precise endo- and phenotyping of COPD and asthma patients but also have direct clinical relevance and can be used for patient-specific prognosis.
More information: https://www.calm-qe.de/
EVA4MII: EVAluation research based on data from routine clinical care 4 the MII
Funded by Federal Ministry of Research, Technology and Space (BMFTR)
Funding period: 2023-2027
In collaboration with the university hospitals in Jena and Würzburg, the EVA4MII project focuses on developing a consulting platform for evaluation research based on routinely collected clinical data from the Medical Informatics Initiative (MII).
More information: