Article(id=1321425756857676578, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1321425717926154257, articleNumber=null, orderNo=null, doi=10.20251/j.cnki.1003-3734.2026.12.002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=1755014400000, acceptedDateStr=2025-08-13, onlineDate=1792982051337, onlineDateStr=2026-10-26, pubDate=1782748800000, pubDateStr=2026-06-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792982051337, onlineIssueDateStr=2026-10-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792982051337, creator=13701087609, updateTime=1792982051337, updator=13701087609, issue=Issue{id=1321425717926154257, tenantId=1146029695717560320, journalId=1235980733773295621, year='2026', volume='35', issue='12', pageStart='1233', pageEnd='1344', issueExtLink='null', onlineDate='null', pubDate='1782748800000', pubDateStr='2026-06-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792982042056, creator='13701087609', updateTime=1792982988069, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321429686186529703, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1321425717926154257, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321429686186529704, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1321425717926154257, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1243, endPage=1250, ext={EN=ArticleExt(id=1321425758413763363, articleId=1321425756857676578, tenantId=1146029695717560320, journalId=1235980733773295621, language=EN, title=Research on patent value evaluation methods for medical universities and colleges, columnId=null, journalTitle=Chinese Journal of New Drugs, columnName=null, runingTitle=null, highlight=null, articleAbstract=
Objective: Given that traditional patent value evaluation methods are easily influenced by subjective factors and struggle to comprehensively and objectively reflect the value of patents, this study proposed a patent value evaluation method to identify high-value patents.
Methods: This study employs information gain and variance analysis methods to perform feature selection on patent data from medical universities, optimizing input features and reducing redundant information. In terms of model construction, a three-layer BP neural network structure was designed, incorporating ReLU activation functions and Dropout techniques to mitigate overfitting issues and enhance the model's ability to handle nonlinear relationships, thereby improving the model's stability and generalization.
Results: The experimental results showed that the model achieved a prediction accuracy of 96%. Feature selection effectively improved the model's performance to a certain extent, demonstrating good results across multiple evaluation metrics and verifying the feasibility of the proposed method.
Conclusion: This study provides a feasible and effective practical method for patent value evaluation in medical universities, offering valuable reference and application significance, particularly in the identification and prediction of high-value patents.
, authors=Jian-bo WANG, Zhao-lian OUYANG, Juan CHEN, Yan LU, Ting ZHANG
*, authorsList=Jian-bo WANG, Zhao-lian OUYANG, Juan CHEN, Yan LU, Ting ZHANG, authorCompany=null, correspAuthors=Ting ZHANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1321425758648644388, articleId=1321425756857676578, tenantId=1146029695717560320, journalId=1235980733773295621, language=CN, title=医学院校专利价值评估方法研究, columnId=1236331162679955859, journalTitle=中国新药杂志, columnName=新药述评与论坛, runingTitle=null, highlight=null, articleAbstract=
目的： 鉴于传统专利价值评估方法易受主观因素影响，难以全面、客观地反映专利的价值，本研究提出了一种专利价值评估方法以识别高价值专利。
方法： 本研究采用信息增益和方差分析方法对医学院校的专利数据进行特征选择，以优化输入特征并减少冗余信息。在模型构建方面，设计了3层BP神经网络结构，并结合ReLU激活函数和Dropout技术，以缓解过拟合问题并提升模型对非线性关系的处理能力，从而增强模型的稳定性和泛化能力。
结果： 实验结果表明，模型的预测准确率达到96%，特征选择在一定程度上有效提升了模型性能，并在多个评价指标上均表现出较好的效果，验证了方法的可行性。
结论： 本研究为医学院校专利价值评估提供了一种可行且有效的实践方法，尤其在高价值专利的识别和预测方面具有一定的参考意义和应用价值。
, authors=王建波, 欧阳昭连, 陈娟, 卢岩, 张婷
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王建波，男，硕士研究生，主要从事医疗器械与药物战略情报研究。E-mail: wangjianbo@student.pumc.edu.cn。
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王建波，男，硕士研究生，主要从事医疗器械与药物战略情报研究。E-mail: wangjianbo@student.pumc.edu.cn。
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