| 向超益,吴亚飞,方亚.数据挖掘技术在心血管疾病预后研究中的应用进展[J].Chinese journal of Epidemiology,2021,42(12):2234-2238 |
| 数据挖掘技术在心血管疾病预后研究中的应用进展 |
| Progress on the application of data mining in the prognosis of cardiovascular disease |
| Received:April 30, 2021 |
| DOI：10.3760/cma.j.cn112338-20210430-00361 |
| KeyWord: 数据挖掘 心血管疾病 预后 预测 |
| English Key Word: Data mining Cardiovascular disease Prognosis Prediction |
| FundProject:国家自然科学基金（81973144） |
| Author Name | Affiliation | E-mail | | Xiang Chaoyi | School of Public Health, Xiamen University, Xiamen 361102, China
Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen 361102, China | | | Wu Yafei | School of Public Health, Xiamen University, Xiamen 361102, China
Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen 361102, China
National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361102, China | | | Fang Ya | School of Public Health, Xiamen University, Xiamen 361102, China
Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen 361102, China
National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361102, China | fangya@xmu.edu.cn |
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| Abstract: |
| 数据挖掘技术在心血管疾病预后研究中得到较为广泛的应用。其中，在脑卒中预后研究中更多涉及干预有效性的预测，而在其他心血管疾病预后研究中，主要关注自然预后和干预安全性的预测。相较于传统的统计方法，机器学习方法尤其是以神经网络为基础的深度学习技术在预测心血管疾病预后方面有更好的性能表现，值得进一步推广。本文梳理了近些年数据挖掘在心血管疾病预后研究中的应用进展，并对当前研究不足进行总结、提出展望。 |
| English Abstract: |
| Data mining has been widely used in the study of cardiovascular disease prognosis. For stroke prognosis, the focus was mainly on the prediction of intervention effectiveness. In contrast, the focus was primarily on predicting natural prognostic and intervention safety for other cardiovascular diseases. In addition, compared with traditional statistical methods, machine learning, especially deep learning based on neural networks has much better performance in predicting the prognosis of cardiovascular diseases, which is worthy of further promotion and application. Therefore, this study systematically reviewed the recent application progress of data mining in cardiovascular disease prognosis, summarized the shortcomings of current studies, and put forward future directions. |
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