Linking a Lab Result to its Test Event in the Clinical Domain
| Název práce v češtině: | Propojování laboratorního výsledku s vyšetřením v klinické péči |
|---|---|
| Název v anglickém jazyce: | Linking a Lab Result to its Test Event in the Clinical Domain |
| Akademický rok vypsání: | 2024/2025 |
| Typ práce: | diplomová práce |
| Jazyk práce: | angličtina |
| Ústav: | Ústav formální a aplikované lingvistiky (32-UFAL) |
| Vedoucí / školitel: | doc. RNDr. Pavel Pecina, Ph.D. |
| Řešitel: |
| Zásady pro vypracování |
| In the world of medical diagnosis, laboratory tests are a common and important step and are documented in clinical statements. Usually these statements include the reasons for a clinical visit, the physical exams undertaken, the assessment of the patient's diagnosis and subsequent treatments. A useful datapoint that can be extracted from these statements are all the laboratory tests and their corresponding results. The task can be considered as that of relation extraction from clinical statements. Usually these statements include the reasons for a clinical visit, the physical exams undertaken, the assessment of the patient's diagnosis and subsequent treatments.
The task consists in identifying test results and measurements and linking them to the textual mentions of the laboratory tests and measurements from which they were obtained. For this, both elements need to be identified in text and then the relation between them needs to be created. The goal of this thesis is to use state-of-the-art techniques to achieve the best relation extraction performance on clinical data. It will also seek to build upon existing work in this domain such as Roy et.al. (2021) and Qi et al. (2020). These papers make use of large pre-trained models such as BERT and also domain adaptation to perform the task of relation extraction in the clinical domain. |
| Seznam odborné literatury |
| Roy, Arpita, and Shimei Pan. Incorporating Medical Knowledge in BERT for Clinical Relation Extraction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 5357–66. Online and Punta Cana, Dominican Republic: Association for Computational Linguistics, 2021. https://doi.org/10.18653/v1/2021.emnlp-main.435.
Wei Q, Ji Z, Si Y, Du J, Wang J, Tiryaki F, Wu S, Tao C, Roberts K, Xu H. Relation Extraction from Clinical Narratives Using Pre-trained Language Models. AMIA Annu Symp Proc. 2020 Mar 4;2019:1236-1245. PMID: 32308921; PMCID: PMC7153059. |