Summary of Insightbuddy-ai: Medication Extraction and Entity Linking Using Large Language Models and Ensemble Learning, by Pablo Romero and Lifeng Han and Goran Nenadic
INSIGHTBUDDY-AI: Medication Extraction and Entity Linking using Large Language Models and Ensemble Learning
by Pablo Romero, Lifeng Han, Goran Nenadic
First submitted to arxiv on: 28 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the application of state-of-the-art language models (LLMs) in text mining tasks related to medications and their attributes. The authors compare the performance of different LLMs, including BERT, RoBERTa, RoBERTa-L, BioBERT, BioClinicalBERT, BioMedRoBERTa, ClinicalBERT, and PubMedBERT, on general and specific domains. Additionally, they explore ensemble learning methods to augment model performances. The results demonstrate that ensemble learning approaches outperform individual fine-tuned base models. Furthermore, the authors develop an entity linking function to map extracted medical terminologies into standard clinical knowledge bases such as SNOMED-CT, BNF, dm+d, and ICD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses powerful computers to help healthcare professionals better understand medicines and their effects. It compares different types of language models to see which one works best for extracting information about medications from text. The results show that combining multiple models can improve accuracy. Additionally, the authors create a tool that maps medical terms into standard formats used in hospitals. |
Keywords
» Artificial intelligence » Bert » Entity linking
