Electronic Health Records (EHRs) are rich repositories of patientinformation, containing structured tabular data (e.g., lab results, diagnoses) and unstructured textual data (e.g., discharge summaries, physician notes). Traditional models often focus on a single modality, missing out on the holistic view provided by combining these data types. Our project, based on real data, seeks to bridge this gap through multi-modality learning, which remains underexplored yet highly promising in healthcare. See [1] for an overview of the research domain.

Type of Theses

• Bachelor´s thesis
• Master´s thesis

Research Focus

Our project aims to develop a personalzied and interpretable recommender system that leverages both tabular and textual data from EHRs.

Ideal Candidate

• Background in machine learning, data science, or related fields.
• Experience or interest in working with healthcare data, particularly EHRs.
• Strong programming skills (Python, PyTorch, Huggingface).
• Enthusiastic about solving complex problems and contributing to impactful research.

Contact Information

For more details and to apply, please contact Prof. Michael Färber at michael.faerber@tu-dresden.de as well as Zhan Qu at zhan.qu@kit.edu.

[1] Wornow, M., Xu, Y., Thapa, R., Patel, B., Steinberg, E., Fleming, S., … & Shah, N. H. (2023). The shaky foundations of large language models and foundation models for electronic health records. npj Digital Medicine, 6(1), 135.