| 基于潜在高被引论文的研究前沿识别 |
| 修订日期：2023-02-13 点此下载全文 |
| 引用本文：李建悦,高健,袁永旭.基于潜在高被引论文的研究前沿识别[J].医学信息学杂志,2023,44(10):56-62 |
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| 基金项目:山西省研究生教育创新项目(项目编号:2020SY242)。 |
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| 中文摘要:目的/意义 构建潜在高被引论文的识别模型,挖掘研究前沿,为前沿识别研究提供新思路。方法/过程 从3个维度确定特征体系,基于6种机器学习算法构建潜在高被引论文识别模型,选择最优模型识别潜在高被引论文,使用聚类等方法对筛选出的文献数据集进行研究前沿挖掘。结果/结论 以阿尔茨海默病人工智能医学领域为例进行实证研究,XGBoost模型性能评估结果最优,有效识别出4类研究前沿主题:智能辅助工具、病症早期预测和分类、生物标志物、病症风险评估和照护。 |
| 中文关键词:潜在高被引论文 研究前沿 机器学习 人工智能 阿尔茨海默病 |
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| Research Frontier Identification Based on Potentially Highly Cited Papers |
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| Abstract:Purpose/Significance To construct the identification model of potentially highly cited papers, to excavate the research frontier, so as to provide new ideas for the research of frontier identification. Method/Process The paper determines the feature system from three dimensions, builds the identification model of potentially highly cited papers based on six machine learning algorithms, selects the best model to identify potentially highly cited papers, and uses clustering and other methods to mine the research frontier of the selected literature datasets.Result/Conclusion Taking the field of artificial intelligence medicine for Alzheimer’s disease as an example, the XGBoost model has the best performance evaluation results. Four types of research frontier topics have been effectively identified:intelligent auxiliary tools, early disease prediction and classification, biomarkers, disease risk assessment and care. |
| keywords:potentially highly cited paper research frontier machine learning artificial intelligence (AI) Alzheimer’s disease |
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