AI-assisted Knowledge Discovery in Biomedical Literature to Support Decision-making in Precision Oncology
This work addresses the challenge of processing biomedical literature for decision-making in precision oncology, but it is incremental as it compares existing models without introducing new methods.
The paper tackled the problem of extracting knowledge from biomedical literature to support precision oncology decisions by evaluating NLP models for named entity recognition and relation extraction, with BioBERT achieving an F1-score of 0.79 in relation extraction and PubTator 3.0 reaching 0.93 in named entity recognition.
The delivery of appropriate targeted therapies to cancer patients requires the complete analysis of the molecular profiling of tumors and the patient's clinical characteristics in the context of existing knowledge and recent findings described in biomedical literature and several other sources. We evaluated the potential contributions of specific natural language processing solutions to support knowledge discovery from biomedical literature. Two models from the Bidirectional Encoder Representations from Transformers (BERT) family, two Large Language Models, and PubTator 3.0 were tested for their ability to support the named entity recognition (NER) and the relation extraction (RE) tasks. PubTator 3.0 and the BioBERT model performed best in the NER task (best F1-score equal to 0.93 and 0.89, respectively), while BioBERT outperformed all other solutions in the RE task (best F1-score 0.79) and a specific use case it was applied to by recognizing nearly all entity mentions and most of the relations.