@inproceedings{romero-etal-2025-insightbuddy,
    title = "{INSIGHTBUDDY}-{AI}: Medication Extraction and Entity Linking using Pre-Trained Language Models and Ensemble Learning",
    author = "Romero, Pablo  and
      Han, Lifeng  and
      Nenadic, Goran",
    editor = "Ebrahimi, Abteen  and
      Haider, Samar  and
      Liu, Emmy  and
      Haider, Sammar  and
      Leonor Pacheco, Maria  and
      Wein, Shira",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)",
    month = apr,
    year = "2025",
    address = "Albuquerque, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2025.naacl-srw.2/",
    pages = "18--27",
    ISBN = "979-8-89176-192-6",
    abstract = "This paper presents our system, InsightBuddy-AI, designed for extracting medication mentions and their associated attributes, and for linking these entities to established clinical terminology resources, including SNOMED-CT, the British National Formulary (BNF), ICD, and the Dictionary of Medicines and Devices (dm+d).To perform medication extraction, we investigated various ensemble learning approaches, including stacked and voting ensembles (using first, average, and max voting methods) built upon eight pre-trained language models (PLMs). These models include general-domain PLMs{---}BERT, RoBERTa, and RoBERTa-Large{---}as well as domain-specific models such as BioBERT, BioClinicalBERT, BioMedRoBERTa, ClinicalBERT, and PubMedBERT.The system targets the extraction of drug-related attributes such as adverse drug effects (ADEs), dosage, duration, form, frequency, reason, route, and strength.Experiments conducted on the n2c2-2018 shared task dataset demonstrate that ensemble learning methods outperformed individually fine-tuned models, with notable improvements of 2.43{\%} in Precision and 1.35{\%} in F1-score.We have also developed cross-platform desktop applications for both entity recognition and entity linking, available for Windows and macOS.The InsightBuddy-AI application is freely accessible for research use at \url{https://github.com/HECTA-UoM/InsightBuddy-AI}."
}