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Using Informative Score for Instance Selection Strategy in Semi-Supervised Sentiment Classification
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作者 Vivian Lee Lay Shan Gan Keng Hoon +1 位作者 Tan Tien Ping Rosni Abdullah 《Computers, Materials & Continua》 SCIE EI 2023年第3期4801-4818,共18页
Sentiment classification is a useful tool to classify reviews about sentiments and attitudes towards a product or service.Existing studies heavily rely on sentiment classification methods that require fully annotated ... Sentiment classification is a useful tool to classify reviews about sentiments and attitudes towards a product or service.Existing studies heavily rely on sentiment classification methods that require fully annotated inputs.However,there is limited labelled text available,making the acquirement process of the fully annotated input costly and labour-intensive.Lately,semi-supervised methods emerge as they require only partially labelled input but perform comparably to supervised methods.Nevertheless,some works reported that the performance of the semi-supervised model degraded after adding unlabelled instances into training.Literature also shows that not all unlabelled instances are equally useful;thus identifying the informative unlabelled instances is beneficial in training a semi-supervised model.To achieve this,an informative score is proposed and incorporated into semisupervised sentiment classification.The evaluation is performed on a semisupervised method without an informative score and with an informative score.By using the informative score in the instance selection strategy to identify informative unlabelled instances,semi-supervised models perform better compared to models that do not incorporate informative scores into their training.Although the performance of semi-supervised models incorporated with an informative score is not able to surpass the supervised models,the results are still found promising as the differences in performance are subtle with a small difference of 2%to 5%,but the number of labelled instances used is greatly reduced from100%to 40%.The best finding of the proposed instance selection strategy is achieved when incorporating an informative score with a baseline confidence score at a 0.5:0.5 ratio using only 40%labelled data. 展开更多
关键词 Document-level sentiment classification semi-supervised learning instance selection informative score
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改良早期预警评分在急诊分诊系统中的应用 被引量:8
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作者 陈建萍 洪凌 +3 位作者 毕东军 杨伟英 孙美萍 施欢欢 《医院管理论坛》 2014年第3期22-23,54,共3页
目的探讨改良早期预警评分(MEWS)在急诊分诊管理中的应用价值。方法选取2011年3月至12月急诊患者65147例为对照组,2012年3至12月急诊患者68092例为观察组。对照组患者按常规的急诊分诊流程处理,观察组患者采用MEWS系统,根据评分结果进... 目的探讨改良早期预警评分(MEWS)在急诊分诊管理中的应用价值。方法选取2011年3月至12月急诊患者65147例为对照组,2012年3至12月急诊患者68092例为观察组。对照组患者按常规的急诊分诊流程处理,观察组患者采用MEWS系统,根据评分结果进行分区分级处置,比较两种分诊流程在急诊患者病情评估中的准确率,比较医生、护士对两种分诊管理模式的满意度。结果观察组分诊准确率高于对照组(P<0.01);医生、护士对实施MEWS系统前后的急诊患者分诊管理满意度分别为86.7%和94.6%(χ2=18.61,P<0.05)。结论 MEWS便于急诊护士更准确地将患者分诊至相应就诊区域,可作为急诊合理分诊患者的有效工具,值得推广。 展开更多
关键词 改良早期预警评分 急诊分诊 信息化
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强直性脊柱炎信息采集系统的实现及应用
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作者 冯静 冯媛 《中国医学教育技术》 2012年第4期447-451,共5页
通过对强直性脊柱炎以及医疗信息系统的调研分析,构建了一个基于Web的强直性脊柱炎信息采集处理软件系统。该系统实现了对强直性脊柱炎患者信息的查询、统计、评分、数据导入导出、用户管理以及药物信息管理等核心功能,可对强直性脊柱... 通过对强直性脊柱炎以及医疗信息系统的调研分析,构建了一个基于Web的强直性脊柱炎信息采集处理软件系统。该系统实现了对强直性脊柱炎患者信息的查询、统计、评分、数据导入导出、用户管理以及药物信息管理等核心功能,可对强直性脊柱炎的发病机制、易感因素以及药物疗效进行深入研究;同时为医务工作者提供了一个良好的研究平台。 展开更多
关键词 强直性脊柱炎 信息化 医疗系统 统计评分
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