【摘要】 目的/意义探讨应用深度学习模型在居家护理场景识别用户需求文本中的护理需求实体，以期通过自动化手段精准识别用户需求，为提升居家护理服务的效率和质量提供技术支持。方法/过程选取560条用户护理需求文本客观数据，基于《国际功能、残疾和健康分类》对文本中的护理需求实体进行分类标注，采用BERT-BiLSTM-CRF模型进行实体识别，通过消融实验验证模型效果，分析实验结果，评估模型性能。结果/结论 BERT-BiLSTM-CRF模型实体级别微平均准确率、召回率、F1值分别为0.752 9、0.775 8、0.764 2,表明该模型可以为居家护理场景下自动化挖掘用户需求、优化护理服务流程和提高护理质量提供有力支持。更多还原

【Abstract】 Purpose/Significance To explore deep learning models can identify care needs entities in user needs texts in home healthcare scenarios, so as to accurately identify user needs through automated means to provide technical support for improving the efficiency and quality of home care services. Method/Process 560 pieces of objective data of user care needs are selected to classify and label the care needs entities in the texts based on the International Classification of Functioning, Disability and Health. The BERT-BiLSTM-CRF model is used to perform the entity recognition task. The effect of the model is verified by the ablation experiment, the experimental results are analyzed, and the performance of the model is evaluated. Result/Conclusion The model’s precision, recall, and F1-score at the entity-level micro-average are 0.752 9, 0.775 8, and 0.764 2, respectively, indicating that the model can provide strong support for automatic mining of user needs, optimization of care service process and improvement of care quality in home healthcare scenarios.更多还原