,

Weiqi Zhai1, Rongze Jiang2, Xiaodi Huang3

  • 1Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, China; Shanghai Key Lab of Intelligent Information Processing and Shanghai Institute of Artificial Intelligence Algorithm, Fudan University, Shanghai, China; Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.

概括

这项研究通过使用实例级同义词和新的HPOBERT模型,提高了生物医学文本中人类现象型本体学 (HPO) 术语识别. 这些方法显著提高了疾病诊断和分析的准确性.

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