Article(id=1322168535988679546, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322168387082502716, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.06.011, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1744214400000, receivedDateStr=2025-04-10, revisedDate=1766505600000, revisedDateStr=2025-12-24, acceptedDate=null, acceptedDateStr=null, onlineDate=1793159143683, onlineDateStr=2026-10-28, pubDate=1774368000000, pubDateStr=2026-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793159143683, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793159143683, creator=13701087609, updateTime=1793159143683, updator=13701087609, issue=Issue{id=1322168387082502716, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='6', pageStart='881', pageEnd='1056', issueExtLink='null', onlineDate='null', pubDate='1774368000000', pubDateStr='2026-03-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793159108180, creator='13701087609', updateTime=1793159108180, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=965, endPage=970, ext={EN=ArticleExt(id=1322168537683178363, articleId=1322168535988679546, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Construction and evaluation of nursing knowledge question answering system based on deep learning, columnId=1220689487877821128, journalTitle=Chinese Nursing Research, columnName=Interdisciplinary Nursing Study, runingTitle=null, highlight=null, articleAbstract=
Objective

To construct a nursing knowledge question answering system based on deep learning technology and to evaluate its application effectiveness in clinical nursing.

Methods

A knowledge base in the nursing domain was constructed using deep learning methods.The pre-trained language models was taken such as Bidirectional encoder representations from transformers(BERT) as the core to develop the nursing knowledge question answering system.The technical performance and clinical application effects of the system were evaluated.

Results

The constructed nursing knowledge question answering system achieved an accuracy of 86.8% on nursing professional questions.The average response time was 1.2 seconds.Clinical application results showed that the system effectively reduced the workload of nurses.The per shift time spent for patients' answering common questions in nurses was decreased by 93.4%.The satisfaction score of the nurse was 4.3.

Conclusions

The nursing knowledge question answering system based on deep learning can effectively enhance nursing work efficiency and provide reliable knowledge support for nurses.

, authors=Jiapei YAO, Xiaofei WANG, Dongmei XU, authorsList=Jiapei YAO, Xiaofei WANG, Dongmei XU, authorCompany=null, correspAuthors=Dongmei XU, authorNote=null, correspAuthorsNote=
XU Dongmei, E-mail:
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目的

基于深度学习技术构建护理知识问答系统,并评估其在临床护理中的应用效果。

方法

采用深度学习方法构建护理领域知识库,以Transformer的双边编码器表示(BERT)等预训练语言模型为核心,开发护理知识问答系统。对系统的技术性能和临床应用效果进行评估。

结果

构建的护理知识问答系统在护理专业问题上的准确率为86.8%,平均响应时间为1.2 s。临床应用结果显示,系统可有效减轻护士工作负担,护士每班回答病人常见问题时间减少93.4%,护士满意度达4.3分。

结论

基于深度学习的护理知识问答系统能有效提升护理工作效率,为护士提供可靠的知识支持。

, authors=姚嘉佩, 王小飞, 徐冬梅*, *, authorsList=姚嘉佩, 王小飞, 徐冬梅, authorCompany=null, correspAuthors=徐冬梅, authorNote=

姚嘉佩,主管护师,硕士研究生

, correspAuthorsNote=
徐冬梅,E-mail:
XU Dongmei, E-mail:
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figureFileBig=GtUCUv4pcjjYhV/WDdhnUA==, tableContent=null), ArticleFig(id=1322530573235634427, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168535988679546, language=CN, label=图5, caption=不同类型护理问题的系统准确率, figureFileSmall=PncFCM9uV2a3UVH0gHwR2g==, figureFileBig=GtUCUv4pcjjYhV/WDdhnUA==, tableContent=null), ArticleFig(id=1322530573315326204, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168535988679546, language=EN, label=Table 1, caption=

Comparison of nursing work efficiency before and after application of the knowledge question answering system(n=83)

, figureFileSmall=null, figureFileBig=null, tableContent=
评估指标系统应用前(min)系统应用后(min)减少率(%)t值(应用前后比较)P
每班回答病人常见问题时间25.8±4.21.7±0.593.453.27<0.001
每班护理文档查询时间14.7±3.12.1±0.685.737.65<0.001
每班专业咨询时间45.6±6.412.3±2.873.045.93<0.001
每班操作流程确认时间18.3±3.73.5±0.980.936.12<0.001
每班新护士指导时间36.2±5.811.4±2.368.539.84<0.001
), ArticleFig(id=1322530573420183805, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168535988679546, language=CN, label=表1, caption=

护理知识问答系统应用前后护士工作效率比较(n=83)

, figureFileSmall=null, figureFileBig=null, tableContent=
评估指标系统应用前(min)系统应用后(min)减少率(%)t值(应用前后比较)P
每班回答病人常见问题时间25.8±4.21.7±0.593.453.27<0.001
每班护理文档查询时间14.7±3.12.1±0.685.737.65<0.001
每班专业咨询时间45.6±6.412.3±2.873.045.93<0.001
每班操作流程确认时间18.3±3.73.5±0.980.936.12<0.001
每班新护士指导时间36.2±5.811.4±2.368.539.84<0.001
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基于深度学习护理知识问答系统的构建与评估
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姚嘉佩 , 王小飞 , 徐冬梅 *, *
护理研究 | 交叉护理学研究 2026,40(6): 965-970
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护理研究 |交叉护理学研究 2026 , 40 (6) : 965 -970
基于深度学习护理知识问答系统的构建与评估
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姚嘉佩, 王小飞, 徐冬梅*, *
作者信息
  • 1南京医科大学常州医学中心,南京医科大学第三附属医院(常州市第二人民医院),江苏 213000
通讯作者:
徐冬梅,E-mail:
XU Dongmei, E-mail:
作者简介:

姚嘉佩,主管护师,硕士研究生

Construction and evaluation of nursing knowledge question answering system based on deep learning
Jiapei YAO, Xiaofei WANG, Dongmei XU
Affiliations
  • 1Changzhou Medical Center, The Second People's Hospital of Changzhou, The Third Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Jiangsu 213000 China
  • Corresponding Author:
    XU Dongmei, E-mail:
出版时间: 2026-03-25 doi: 10.12102/j.issn.1009-6493.2026.06.011
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目的

基于深度学习技术构建护理知识问答系统,并评估其在临床护理中的应用效果。

方法

采用深度学习方法构建护理领域知识库,以Transformer的双边编码器表示(BERT)等预训练语言模型为核心,开发护理知识问答系统。对系统的技术性能和临床应用效果进行评估。

结果

构建的护理知识问答系统在护理专业问题上的准确率为86.8%,平均响应时间为1.2 s。临床应用结果显示,系统可有效减轻护士工作负担,护士每班回答病人常见问题时间减少93.4%,护士满意度达4.3分。

结论

基于深度学习的护理知识问答系统能有效提升护理工作效率,为护士提供可靠的知识支持。

深度学习  /  人工智能  /  知识问答系统  /  自然语言处理  /  护理信息化  /  护理管理
Objective

To construct a nursing knowledge question answering system based on deep learning technology and to evaluate its application effectiveness in clinical nursing.

Methods

A knowledge base in the nursing domain was constructed using deep learning methods.The pre-trained language models was taken such as Bidirectional encoder representations from transformers(BERT) as the core to develop the nursing knowledge question answering system.The technical performance and clinical application effects of the system were evaluated.

Results

The constructed nursing knowledge question answering system achieved an accuracy of 86.8% on nursing professional questions.The average response time was 1.2 seconds.Clinical application results showed that the system effectively reduced the workload of nurses.The per shift time spent for patients' answering common questions in nurses was decreased by 93.4%.The satisfaction score of the nurse was 4.3.

Conclusions

The nursing knowledge question answering system based on deep learning can effectively enhance nursing work efficiency and provide reliable knowledge support for nurses.

deep learning  /  artificial intelligence  /  knowledge question answering system  /  natural language processing  /  nursing informatization  /  nursing management
姚嘉佩, 王小飞, 徐冬梅. 基于深度学习护理知识问答系统的构建与评估. 护理研究, 2026 , 40 (6) : 965 -970 . DOI: 10.12102/j.issn.1009-6493.2026.06.011
Jiapei YAO, Xiaofei WANG, Dongmei XU. Construction and evaluation of nursing knowledge question answering system based on deep learning[J]. Chinese Nursing Research, 2026 , 40 (6) : 965 -970 . DOI: 10.12102/j.issn.1009-6493.2026.06.011
随着人工智能(AI)技术的迅速发展,深度学习在医疗健康领域的应用日益广泛[1-2]。护理工作作为医疗服务的重要组成部分,面临工作量大、知识更新快、病人需求多样化等挑战。护士在日常工作中需要回答大量来自病人及家属的问题,同时还需及时获取专业知识以支持临床决策,这给护士带来了巨大的工作负担[3]。因此,开发一种能够辅助护士高效获取专业知识并快速回答常见问题的智能系统具有重要意义。深度学习是AI的核心技术,其通过多层神经网络从数据中学习特征,已在自然语言处理领域取得突破性进展[4]。近年来,以双向编码器表征(BERT)、RoBERTa等为代表的预训练语言模型通过自监督学习获得了强大的语言理解能力[5],为构建专业领域的知识问答系统提供了技术基础。目前,国内外已有多项研究将深度学习应用于护理领域,如智能健康管理[6]、护理决策支持[7]等,但专门针对护士日常工作的知识问答系统研究相对不足。本研究基于深度学习技术构建护理知识问答系统,并评估其在临床环境中的应用效果,以期为护理工作的智能化提供新思路。
护理知识问答系统以深度学习模型为核心,主要包括知识库构建模块、问题理解模块、答案检索与生成模块、用户交互界面模块4个功能模块,各功能模块有机整合,其中,知识库构建模块负责收集和结构化处理护理专业资料;问题理解模块负责识别用户问题意图并进行复杂问题拆解;答案检索与生成模块基于深度学习从相关文档中提取答案;用户交互界面模块提供友好的Web端和移动端操作体验,详见图1。4个模块围绕中心的深度学习模型协同工作,形成完整的智能问答流程。
1)护理专业教材与指南,如《基础护理学》《临床护理技术操作规范》等;2)护理专业期刊文献;3)临床护理常见问题集;4)医院规章制度与流程文件。所有资料均经过专家审核确认内容准确性。
对收集的文本资料进行清洗、分词、去重等预处理,采用TF-IDF和TextRank算法提取关键信息,构建结构化知识条目。同时,将相似问题进行聚类,建立问题-答案对,形成初步问答语料库。
选用基于Transformer架构的深度学习模型,采用预训练-微调范式,基础模型选择中文BERT-base模型,针对护理领域知识进行继续预训练,增强模型对护理专业术语的理解能力。
模型训练分为继续预训练和微调训练2个阶段。首先,使用收集的护理文本资料进行掩码语言模型训练,使模型适应护理领域语言特点;然后基于构建的问答语料库进行监督微调,优化模型在护理问答任务方面的表现。训练采用Adam优化器,学习率设为2e-5,batch size为32,训练10个epoch。为防止过拟合,采用early stopping策略,在验证集连续3个epoch无提升则停止训练。
深度学习模型以Transformer的BERT预训练语言模型为基础,主要包括3个层次,即输入层、编码层和输出层,其中,输入层接收问题和相关文本,通过特殊标记[CLS]和[SEP]将二者连接成单一序列输入至模型;编码层利用多头自注意力机制对输入序列进行双向上下文编码,捕捉词语间的长距离依赖关系和语义信息;输出层基于编码表示预测答案或生成响应。详见图2。模型通过大规模预训练获得通用语言理解能力,再通过领域适应性训练和微调,增强对护理专业术语和知识的理解能力。
深度学习模型在训练过程中的损失曲线显示,随着训练轮次增加,训练损失率和验证损失率均呈现下降趋势,但在第8轮后验证损失下降不明显,表明模型逐渐收敛。详见图3。
模型不同阶段训练准确率曲线显示,训练准确率和验证准确率均随着训练轮次增加而提高,并在第8轮达到最佳验证准确率(87.6%)。根据early stopping策略,在连续3个epoch验证集性能无提升后停止训练,最终选择第8轮模型参数作为最优模型。详见图4。
通过深度学习意图分类模型识别用户问题类型,包括知识咨询、操作指导、政策查询等。对于复杂问题,系统采用问题拆解策略,将其分解为多个简单子问题进行处理。
系统采用两阶段检索-生成框架,首先基于BM25和基于深度学习的语义相似度算法从知识库检索相关文档;然后利用深度学习模型从检索结果中抽取或生成最终答案。对于无法回答的问题,系统会给出明确提示并记录,用于后续知识库更新。
包括Web端和移动端,支持文字输入和语音交互,界面设计符合护士使用习惯。系统具有问题推荐、历史记录查询等功能,并能根据用户反馈不断优化回答质量。
从准确率、F1值、响应时间维度评估系统技术性能。针对5类护理测试问题,分别构建各含500道题的测试集,总计2 500道题,由3名高级职称临床护士进行答案标注,作为评估基准。
选取常州市某三级甲等医院5个临床科室(共83名护士)开展为期1个月的系统试用。评估内容包括护士满意度、系统使用情况、工作效率、访谈收集改进建议。本研究已通过常州市第二人民医院伦理委员会审批(编号:〔2025〕KY010-01)。
采用SPSS 25.0软件进行数据分析,定量资料符合正态分布以均数±标准差(x±s)表示,采用t检验;定性资料以频数和百分比(%)表示,采用χ2检验或Wilcoxon秩和检验。以P<0.05为差异有统计学意义。
5类护理测试问题中,系统平均准确率为86.8%,平均F1值为0.83,说明系统在答案的精确性和完整性方面表现良好。进一步分析发现,系统在不同类型护理问题上的表现存在差异,其中,护理基础知识类问题准确率最高(92.2%),其次为操作技术指导类问题(89.8%)和专科护理知识类问题(88.2%),急救措施类问题准确率为85.2%,护理政策法规类问题准确率最低(78.6%),表明深度学习模型在结构化、标准化程度高的护理基础知识方面表现优异,而在政策法规等动态变化、表述相对复杂的内容方面表现相对不佳。见图5。
系统平均响应时间为1.2 s,其中95%的问题响应时间<2 s,满足临床实时应用需求。在并发测试中,系统在100人同时访问的情况下,性能降低不超过15%,表现出良好的稳定性。
护士满意度调查显示,系统在易用性、回答质量、响应速度方面的均分为4.3分(满分5.0分)。
试用期间,系统总计接收问题12 568次,日均使用139.6次。问题类型分布为护理技术指导(42.3%)、护理知识咨询(31.7%)、政策规定查询(15.6%)、其他(10.4%)。使用高峰期为每日早晨交班后和15:00—17:00。
应用该系统后,护士每班回答病人常见问题时间减少93.4%,每班护理文档查询时间减少85.7%,护士每班专业咨询时间从平均45.6 min减少为12.3 min。详见表1。
通过深入访谈,护士普遍认为系统在以下方面提供了帮助:1)快速获取专业知识,减少查阅文献时间;2)规范操作流程回答,减少差错风险;3)减轻解答重复问题的负担;4)为新入职护士提供即时学习支持。同时,护士也提出了改进建议,如增加图片识别功能、优化专科问题回答质量等。
本研究构建的护理知识问答系统总体准确率达86.8%,其中护理基础知识类问题准确率最高,而护理政策法规类问题准确率最低,表明系统对结构化、标准化程度高的内容理解能力较强。与同类研究相比,本研究结果高于Sutton等[8]的护理决策支持系统准确率(84%),这可能是由于本研究采用深度学习预训练-微调范式并构建了针对性较强的护理专业知识库,而国外研究普遍采用通用医疗语料进行训练,对护理专业领域适应性较弱。未来可通过扩充专科知识库优化深度学习模型结构,进一步提高系统对复杂问题和政策法规类内容的理解能力,特别是加强对动态更新内容的学习机制。
本研究构建的护理知识问答系统平均响应时间为1.2 s,95%的问题响应时间<2 s,且在高并发环境下性能降低不超过15%,展现了良好的实时性和稳定性。与Barlybayev等[9]报告的复杂医疗问答系统3.5 s的平均响应时间和Hunt等[10]研发的临床决策支持系统在多人并发时性能降低25%的情况相比,本系统的性能表现较好。目前,我国研究普遍关注模型准确性而忽视实际应用环境中的响应速度和并发能力,而国外研究更注重系统的工程实现和用户体验。提示我国研究者在护理智能系统开发中,应同时注重模型性能和工程实现,通过模型轻量化、算法优化和硬件加速等技术手段,在保证回答质量的同时提升系统响应效率,为临床一线护士提供流畅的使用体验。
本研究结果显示,护士每班回答病人常见问题时间减少93.4%,每班护理文档查询时间减少85.7%,护士每班专业咨询时间从平均45.6 min减少为12.3 min,有效减轻了护士工作负担。与Huang等[11]报告的护理AI辅助工具可减少重复性工作时间的研究结果相似。国外研究更强调系统对护士职业倦怠的缓解作用,而我国研究更关注系统对工作效率提升的直接效果,表明不同国家护理发展阶段和关注重点存在差异。未来护理智能系统的开发应在提升工作效率的基础上,加强对护士职业满意度和心理健康的关注,通过技术手段减轻护士心理负担,促进护理队伍稳定和专业发展。
本研究构建的护理知识问答系统在临床应用中获得了4.3分的护士满意度评分,尤其在易用性和回答质量方面表现突出,同时发现年轻护士对系统的接受度和满意度较高,此与Alruwaili等[12]的研究结果相似,但国外研究更强调技术伦理和隐私保护对系统接受度的影响,而我国研究更关注系统功能与需求匹配度。表明护理智能系统在不同文化和医疗环境中的接受因素存在差异。未来系统开发应充分考虑不同年龄护士的需求差异,采用分层培训策略提升技术接受度,同时加强系统的透明度和可解释性设计,减轻护士对AI技术的顾虑,促进智能系统在临床的广泛应用。
访谈结果显示,系统对新入职护士提供了有效的即时学习支持,成为护理教育的有益补充,与Seibert等[13]的研究结果相似。目前,国外研究者已开始探索模拟临床情境的沉浸式学习系统和自适应学习路径设计,我国关于智能系统在护理教育中的应用研究相对不足,提示未来护理智能系统应向教育与实践一体化方向发展,通过情景模拟、案例分析、个性化学习路径等功能,打造集知识获取、技能训练和决策支持于一体的综合性护理智能平台,更好地满足不同阶段护士的专业发展需求。
本系统在多模态输入能力和专科适应性方面存在一定局限性。这也是当前护理智能问答系统共同面临的技术挑战,但国外研究在多模态深度学习方面发展较快,Anisuzzaman等[14]已开发出支持图像识别的护理AI系统,能够辅助伤口评估和分类。我国的研究主要关注特定专科知识库的构建,如丁元旗等[15]针对肿瘤专科护理的知识图谱进行研究。提示未来护理智能系统的发展应朝多模态交互和专科深度适应两个方向并行发:一方面,通过整合计算机视觉与自然语言处理技术,实现文本、图像、语音等多模态信息的融合理解;另一方面,针对不同专科特点和需求,定制专科化知识库和功能模块,提高系统在专科护理领域的应用深度和精准度。
本系统设计定位为辅助工具,旨在提升护理人员的工作效率,而非替代其专业判断。在涉及复杂情境判断和伦理问题时,系统提供参考信息,最终决策仍需由专业护理人员做出。有国外学者强调AI应作为医护人员的辅助工具而非替代品[16],其更注重人机协作框架的构建和责任边界的界定,而我国的研究更关注技术应用模式创新[17]。未来护理智能系统的发展方向应是以人为中心的人机协作模式,而非完全自动化。需要深入研究护士与AI系统的最优协作方式,明确系统的辅助定位,在提升护理效率的同时保留护士的专业判断和情感关怀能力,形成人机互补、相互增强的智慧护理新模式,真正实现技术赋能护理实践目标。
基于深度学习的护理知识问答系统能够为护士提供高效、准确的专业知识支持,有效减轻护士工作负担,提升工作效率。系统在临床应用中展现了良好的实用价值,为护理工作的智能化提供了新思路。未来研究应着眼于系统功能的完善和应用模式优化,为构建智慧护理生态系统奠定基础。

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doi: 10.12102/j.issn.1009-6493.2026.06.011
  • 接收时间:2025-04-10
  • 首发时间:2026-10-28
  • 出版时间:2026-03-25
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  • 收稿日期:2025-04-10
  • 修回日期:2025-12-24
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    1南京医科大学常州医学中心,南京医科大学第三附属医院(常州市第二人民医院),江苏 213000

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