Towards Enhancing Large Language Models with SNOMED CT for Multi-document Patient Records Summarization
At a glance
- Project leader : Dr. Ahmad Aghaebrahimian
- Project team : Dr. Jan Bartussek
- Project status : ongoing
- Funding partner : Internal (ZHAW digital / Digital Futures Fund)
- Contact person : Ahmad Aghaebrahimian
Description
Clinical physicians spend about 40% of their work time for reading and writing patient documentation. We will employ NLP, SNOMED CT, and Large Language Models (LLM) to generate concise, accurate, and interoperable summaries of patients’ records, thus saving time, effort, and resources.