Context:
Patient records are a goldmine for various data analytics and machine learning applications aiming at gaining insights that could lead to more efficient treatments, inform innovative clinical decision support systems and enable patient empowerment. However, a lot of information in patient records is "locked" in the form of unstructured text that is not readily amenable to machine processing.
NLP models (the best of them typically based on transformer architectures / Large Language Models these days) can extract named entities from texts (e.g., names of diseases, clinical procedures or drugs). Associating the extracted entities with unique identifiers via standard biomedical vocabularies (such as those represented in the Unified Medical Language System) is still a challenge, though, despite being of great use in the downstream processing of the identified terms.

Goals:
- Study the state of the art in biomedical named entity recognition.
- Review biomedical vocabularies best suited to the task.
- Implement a technique for mapping entities extracted by a bespoke biomedical language model (developed by another student) to the selected vocabulary.
- Validate the produced mappings, ideally based on a feedback of a clinical expert.
- Write up the results in a thesis form.

Requirements:
- Keen interest in the topic.
- At least minimal hands-on experience with natural language processing.
- While the thesis can be written and defended in Czech, its elaboration and presentation in English will be supported enthusiastically (the results may be disseminated to and used by partners in ongoing or future EU projects).
- Monthly (or more frequent, if needed) progress review meetings with the supervisor will be expected.
- The student(s) will also be expected to develop and document any related code using the FI MU Gitlab and ICS SensitiveCloud platforms, and (if applicable) re-use and interact with other related projects there.