Pinard Christopher J, Poon Andrew C, Lagree Andrew, Wu Kuan-Chuen, Li Jiaxu, Tran William T
Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada.
Department of Oncology, Lakeshore Animal Health Partners, Mississauga, Ontario, Canada.
Vet Comp Oncol. 2025 Mar;23(1):102-108. doi: 10.1111/vco.13035. Epub 2024 Dec 23.
Integrating Artificial Intelligence (AI) through Natural Language Processing (NLP) can improve veterinary medical oncology clinical record analytics. Named Entity Recognition (NER), a critical component of NLP, can facilitate efficient data extraction and automated labelling for research and clinical decision-making. This study assesses the efficacy of the Bio-Epidemiology-NER (BioEN), an open-source NER developed using human epidemiological and medical data, on veterinary medical oncology records. The NER's performance was compared with manual annotations by a veterinary medical oncologist and a veterinary intern. Evaluation metrics included Jaccard similarity, intra-rater reliability, ROUGE scores, and standard NER performance metrics (precision, recall, F1-score). Results indicate poor direct translatability to veterinary medical oncology record text and room for improvement in the NER's performance, with precision, recall, and F1-score suggesting a marginally better alignment with the oncologist than the intern. While challenges remain, these insights contribute to the ongoing development of AI tools tailored for veterinary healthcare and highlight the need for veterinary-specific models.
通过自然语言处理（NLP）整合人工智能（AI）可以改善兽医肿瘤学临床记录分析。命名实体识别（NER）是NLP的一个关键组成部分，它可以促进高效的数据提取以及为研究和临床决策进行自动标注。本研究评估了生物流行病学命名实体识别（BioEN）（一种利用人类流行病学和医学数据开发的开源NER）在兽医肿瘤学记录方面的效果。将该NER的性能与一位兽医肿瘤学家和一名兽医实习生的手动注释进行了比较。评估指标包括杰卡德相似度、评分者内部信度、ROUGE分数以及标准的NER性能指标（精确率、召回率、F1分数）。结果表明，该NER对兽医肿瘤学记录文本的直接可翻译性较差，其性能仍有提升空间，精确率、召回率和F1分数表明它与肿瘤学家的一致性略高于实习生。尽管挑战依然存在，但这些见解有助于为兽医医疗量身定制的人工智能工具的持续开发，并凸显了针对兽医的特定模型的必要性。
