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  ---
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  # Greek Medical NER
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  ## Use
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  - **Primary Use Case**: This model is designed to extract medical entities such as symptoms, diagnostic tests, and treatments from clinical text in the Greek language.
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  - **Applications**: Suitable for healthcare professionals, clinical data analysis, and research into medical text processing.
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  ## Training Data
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  - **Data Sources**: Annotated datasets, including clinical data and translations of English medical text into Greek.
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  - **Data Augmentation**: The training dataset underwent data augmentation techniques to improve the model's ability to generalize to different text structures.
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- - **Dataset Split**:
 
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  - **Training Set**: 80%
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  - **Validation Set**: 10%
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  - **Test Set**: 10%
 
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  ---
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  # Greek Medical NER
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+ ## Acknowledgement
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+
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+ This model had been created as part of joint research of HUMADEX research group (https://www.linkedin.com/company/101563689/) and has received funding by the European Union Horizon Europe Research and Innovation Program project SMILE (grant number 101080923) and Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks, project BosomShield ((rant number 101073222). Responsibility for the information and views expressed herein lies entirely with the authors.
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+ Authors:
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+ dr. Izidor Mlakar, Rigon Sallauka, dr. Umut Arioz, dr. Matej Rojc
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+
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  ## Use
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  - **Primary Use Case**: This model is designed to extract medical entities such as symptoms, diagnostic tests, and treatments from clinical text in the Greek language.
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  - **Applications**: Suitable for healthcare professionals, clinical data analysis, and research into medical text processing.
 
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  ## Training Data
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  - **Data Sources**: Annotated datasets, including clinical data and translations of English medical text into Greek.
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  - **Data Augmentation**: The training dataset underwent data augmentation techniques to improve the model's ability to generalize to different text structures.
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+ - **Dataset Split
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+ **:
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  - **Training Set**: 80%
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  - **Validation Set**: 10%
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  - **Test Set**: 10%