Summary of Ai-assisted Knowledge Discovery in Biomedical Literature to Support Decision-making in Precision Oncology, by Ting He et al.
AI-assisted Knowledge Discovery in Biomedical Literature to Support Decision-making in Precision Oncology
by Ting He, Kory Kreimeyer, Mimi Najjar, Jonathan Spiker, Maria Fatteh, Valsamo Anagnostou, Taxiarchis Botsis
First submitted to arxiv on: 12 Dec 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 abstract discusses the importance of analyzing tumor molecular profiling and clinical characteristics to deliver targeted therapies to cancer patients. It also explores the potential contributions of natural language processing (NLP) solutions in biomedical literature analysis. Two models from the BERT family, two Large Language Models, and PubTator 3.0 were evaluated for their ability to support named entity recognition (NER) and relation extraction (RE) tasks. The results show that PubTator 3.0 and BioBERT performed well in NER, while BioBERT outperformed others in RE, recognizing nearly all entities and most relations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding the right treatment for cancer patients by looking at their tumor and medical information. It uses computers to understand biomedical text and finds important details like people, places, and relationships. Two types of computer models were tested: BERT models and Large Language Models. The best ones were PubTator 3.0 and BioBERT, which did a great job identifying key words and connections. |
Keywords
» Artificial intelligence » Bert » Named entity recognition » Natural language processing » Ner » Nlp
