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Wang, Yi

Yi Wang

Frau M.Sc.

Wissenschaftliche Mitarbeiterin
IPVS
Analytic Computing

Kontakt

+49 711 685 88133
E-Mail

Universitätsstraße 32
70569 Stuttgart
Deutschland
Raum: 2.312a

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Publikationen

2.
Diaz Ochoa, J. G., Mustafa, F. E., Weil, F., Wang, Y., Kama, K., & Knott, M. (2024). The aluminum standard: using generative Artificial Intelligence tools to synthesize and annotate non-structured patient data. BMC Medical Informatics and Decision Making, 24, Article 1. https://doi.org/10.1186/s12911-024-02825-4
- BibTeX
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BibTeX
@article{diazochoa2024aluminum, abstract = {Medical narratives are fundamental to the correct identification of a patient’s health condition. This is not only because it describes the patient’s situation. It also contains relevant information about the patient’s context and health state evolution. Narratives are usually vague and cannot be categorized easily. On the other hand, once the patient’s situation is correctly identified based on a narrative, it is then possible to map the patient’s situation into precise classification schemas and ontologies that are machine-readable. To this end, language models can be trained to read and extract elements from these narratives. However, the main problem is the lack of data for model identification and model training in languages other than English. First, gold standard annotations are usually not available due to the high level of data protection for patient data. Second, gold standard annotations (if available) are difficult to access. Alternative available data, like MIMIC (Sci Data 3:1, 2016) is written in English and for specific patient conditions like intensive care. Thus, when model training is required for other types of patients, like oncology (and not intensive care), this could lead to bias. To facilitate clinical narrative model training, a method for creating high-quality synthetic narratives is needed.}, author = {Diaz Ochoa, Juan G. and Mustafa, Faizan E. and Weil, Felix and Wang, Yi and Kama, Kudret and Knott, Markus}, doi = {10.1186/s12911-024-02825-4}, issn = {14726947}, journal = {BMC Medical Informatics and Decision Making}, number = 1, pages = {409--}, refid = {Diaz Ochoa2024}, title = {The aluminum standard: using generative Artificial Intelligence tools to synthesize and annotate non-structured patient data}, url = {https://doi.org/10.1186/s12911-024-02825-4}, volume = 24, year = 2024 }
Link
https://doi.org/10.1186/s12911-024-02825-4
1.
Wang, Y., Dima, G.-C., & Staab, S. (2023). WikiMed-DE : Constructing a Silver-Standard Dataset for German Biomedical Entity Linking using Wikipedia and Wikidata. In L.-A. Kaffee, S. Razniewski, K. Alghamdi, & H. Arnaout (eds.), Proceedings of the Wikidata Workshop 2023 (No. 3640). RWTH. https://ceur-ws.org/Vol-3640/
- BibTeX
- Link
BibTeX
@inproceedings{wang2023wikimedde, address = {Aachen}, author = {Wang, Yi and Dima, Gina-Corina and Staab, Steffen}, booktitle = {Proceedings of the Wikidata Workshop 2023}, editor = {Kaffee, Lucie-Aimée and Razniewski, Simon and Alghamdi, Kholoud and Arnaout, Hiba}, eventdate = {2023-11-13}, eventtitle = {4th Wikidata Workshop, co-located with 22nd International Semantic Web Conference (ISWC 2023)}, language = {eng}, number = 3640, publisher = {RWTH}, series = {CEUR Workshop Proceedings}, title = {WikiMed-DE : Constructing a Silver-Standard Dataset for German Biomedical Entity Linking using Wikipedia and Wikidata}, url = {https://ceur-ws.org/Vol-3640/}, venue = {Athens, Greece}, year = 2023 }
Link
https://ceur-ws.org/Vol-3640/

Yi Wang

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Yi Wang

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