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=
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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
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1Changzhou Medical Center, The Second People's Hospital of Changzhou, The Third Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Jiangsu 213000 China), AuthorCompanyExt(id=1322530568621899990, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168535988679546, companyId=1322530568600928468, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=
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)])], figs=[ArticleFig(id=1322530572174475506, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168535988679546, language=EN, label=Figure 1, caption=
Overall architecture of the nursing knowledge question answering system based on deep learning, figureFileSmall=mn8h7eMPUG1EAgCDQDPzgA==, figureFileBig=4gkXE22Or7kSH/AO/UIe0Q==, tableContent=null), ArticleFig(id=1322530572241584371, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168535988679546, language=CN, label=图1, caption=
基于深度学习的护理知识问答系统整体架构, figureFileSmall=mn8h7eMPUG1EAgCDQDPzgA==, figureFileBig=4gkXE22Or7kSH/AO/UIe0Q==, tableContent=null), ArticleFig(id=1322530572526797044, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168535988679546, language=EN, label=Figure 2, caption=
Schematic diagram of working principle of the deep learning model, figureFileSmall=nfxv6MK7oOSeuB5ik/QsXw==, figureFileBig=SJeuGh9OJhPdRGTjpHiDKA==, tableContent=null), ArticleFig(id=1322530572602294517, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168535988679546, language=CN, label=图2, caption=
深度学习模型工作原理示意图, figureFileSmall=nfxv6MK7oOSeuB5ik/QsXw==, figureFileBig=SJeuGh9OJhPdRGTjpHiDKA==, tableContent=null), ArticleFig(id=1322530572669403382, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168535988679546, language=EN, label=Figure 3, caption=
Training loss curves of the model at different stages, figureFileSmall=cLUhsd1lyvQfQdBq+k7mIQ==, figureFileBig=wDgyOSkpVaEwZ55bjQMeBA==, tableContent=null), ArticleFig(id=1322530572925255927, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168535988679546, language=CN, label=图3, caption=
模型不同阶段训练损失曲线, figureFileSmall=cLUhsd1lyvQfQdBq+k7mIQ==, figureFileBig=wDgyOSkpVaEwZ55bjQMeBA==, tableContent=null), ArticleFig(id=1322530572992364792, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168535988679546, language=EN, label=Figure 4, caption=
Training accuracy curves of the model at different stages, figureFileSmall=5duYAEZ/xxyNOm94CBUxQg==, figureFileBig=nVWqPz04woORMQFiPbMUrw==, tableContent=null), ArticleFig(id=1322530573072056569, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168535988679546, language=CN, label=图4, caption=
模型不同阶段训练准确率曲线, figureFileSmall=5duYAEZ/xxyNOm94CBUxQg==, figureFileBig=nVWqPz04woORMQFiPbMUrw==, tableContent=null), ArticleFig(id=1322530573164331258, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322168535988679546, language=EN, label=Figure 5, caption=
System accuracy for different types of nursing questions, figureFileSmall=PncFCM9uV2a3UVH0gHwR2g==, 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±4.2 | 1.7±0.5 | 93.4 | 53.27 | <0.001 |
| 每班护理文档查询时间 | 14.7±3.1 | 2.1±0.6 | 85.7 | 37.65 | <0.001 |
| 每班专业咨询时间 | 45.6±6.4 | 12.3±2.8 | 73.0 | 45.93 | <0.001 |
| 每班操作流程确认时间 | 18.3±3.7 | 3.5±0.9 | 80.9 | 36.12 | <0.001 |
| 每班新护士指导时间 | 36.2±5.8 | 11.4±2.3 | 68.5 | 39.84 | <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±4.2 | 1.7±0.5 | 93.4 | 53.27 | <0.001 |
| 每班护理文档查询时间 | 14.7±3.1 | 2.1±0.6 | 85.7 | 37.65 | <0.001 |
| 每班专业咨询时间 | 45.6±6.4 | 12.3±2.8 | 73.0 | 45.93 | <0.001 |
| 每班操作流程确认时间 | 18.3±3.7 | 3.5±0.9 | 80.9 | 36.12 | <0.001 |
| 每班新护士指导时间 | 36.2±5.8 | 11.4±2.3 | 68.5 | 39.84 | <0.001 |
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