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    CURRENT RESEARCH

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    Mechanistic Understanding and Prediction of Drug Interactions in Humans using LLM-based Explainable Multimodal Framework

    - To enable the prediction of drug interactions in humans based on the mechanistic understanding
    - To improve the explainability of the in-human prediction results using curated mechanistic explanations
    - To propose technical solutions to incorporate knowledge-graph instilled multimodal drug features and conformal prediction driven input selection strategy

    Risk of MASLD in Patients with Type 2 Diabetes Treated with GLP-1 Receptor Agonists Using Real-World Data

    - To evaluate the preventive effect of GLP-1RAs on MASLD development in patients with T2DM
    - To assess differences in MASLD incidence among diabetes blood-glucose–lowering treatment groups
    - To characterize real-world clinical practice, including treatment patterns and prescribing trends of GLP-1RAs and other antidiabetic agents

    Development of a Large-Scale Korean-English Corpus and a Document-Level Translation Model for Pharmaceutical Field

    - To build a large-scale Korean-English parallel and monolingual corpus specialized for the pharmaceutical field to reflect domain-specific terminology and maintain consistent translation quality
    - To conduct expert validation and ensure translation quality
    - To develop and enhance a -level neural machine translation model and a large language model specialized for the medical and pharmaceutical fields

    Improving generalization performance of Natural Language Procesing Models to extract drug safety information from multi-institutional clinical notes

    - To improve the generalizability of a natural language processing model in extracting adverse drug reaction signal from clinical notes.
    - To test the feasibility of using clinical notes generated in diverse clinical settings (e.g., inpatient, outpatient, practitioner specialty, ER visit, operating room) as a means to improve model’s generalizability.
    - To test the applicability of the large language models (e.g., GPT4) in improving model robustness.

    Development of a registry and generation of real-world evidence using electronic medical record-based real-world data for pediatric acute lymphoblastic leukemia

    - To develop a disease registry for pediatric acute lymphoblastic leukemia (ALL) using real-world data (RWD) from the clinical data warehouse (CDW) in multi-center
    - To generate real-world evidence by conducting 3 observational studies for pediatric ALL using electronic medical record (EMR)-based RWD from the common data model (CDM), the CDW, and the registry newly developed in this study
    - To identify opportunities and challenges in the use of real-world data from EMRs to inform regulatory decisions

    CCADD

    Center for Convergence Approaches in Drug Development, Graduate School of Convergence Science and Technology, Seoul National University

    Room C-208, 145 Gwanggyo-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16229, SOUTH KOREA (Gwanggyo)

    Room 406, Building 17, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, SOUTH KOREA (Yeon-gun)

    Tel: +82-31-888-9189 (Gwanggyo); +82-2-3668-7381 (Yeon-gun)

    Fax: +82-31-888-9575

    Email: ccadd.snu@gmail.com