INSIGHTBUDDY-AI: Medication Extraction and Entity Linking using Large Language Models and Ensemble Learning
<|reference_start|>INSIGHTBUDDY-AI: Medication Extraction and Entity Linking using Large Language Models and Ensemble Learning: Medication Extraction and Mining play an important role in healthcare NLP research due to its practical applications in hospital settings, such as their mapping into standard clinical knowledge bases (SNOMED-CT, BNF, etc.). In this work, we investigate state-of-the-art LLMs in text mining tasks on medications and their related attributes such as dosage, route, strength, and adverse effects. In addition, we explore different ensemble learning methods (\textsc{Stack-Ensemble} and \textsc{Voting-Ensemble}) to augment the model performances from individual LLMs. Our ensemble learning result demonstrated better performances than individually fine-tuned base models BERT, RoBERTa, RoBERTa-L, BioBERT, BioClinicalBERT, BioMedRoBERTa, ClinicalBERT, and PubMedBERT across general and specific domains. Finally, we build up an entity linking function to map extracted medical terminologies into the SNOMED-CT codes and the British National Formulary (BNF) codes, which are further mapped to the Dictionary of Medicines and Devices (dm+d), and ICD. Our model's toolkit and desktop applications are publicly available at \url{https://github.com/HECTA-UoM/ensemble-NER}.<|reference_end|>
@article{romero2024insightbuddy-ai:,
    title={INSIGHTBUDDY-AI: Medication Extraction and Entity Linking using Large
  Language Models and Ensemble Learning},
    author={Pablo Romero and Lifeng Han and Goran Nenadic},
    journal={arXiv preprint arXiv:2409.19467},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19467},
    primaryClass={cs.CL cs.AI}
}