INSIGHTBUDDY-AI: Medication Extraction and Entity Linking using Large Language Models and Ensemble Learning

• URL: http://arxiv.org/abs/2409.19467v2
• Date: Fri, 27 Dec 2024 20:53:02 GMT
• Title: INSIGHTBUDDY-AI: Medication Extraction and Entity Linking using Large Language Models and Ensemble Learning
• Authors: Pablo Romero, Lifeng Han, Goran Nenadic,
• Abstract summary: We build up an entity linking function to map extracted medical terminologies into the SNOMED-CT codes and the British National Formulary codes.<n>Our model's toolkit and desktop applications are publicly available.
• Score: 6.849511893206566
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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}).

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