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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:

30

期数:

2020年01期

页码:

144-148

栏目:

应用开发研究

出版日期:

2020-01-10

文章信息/Info

Title:

Application of Artificial Intelligence Technology in MIMIC Database Mining

文章编号:

1673-629X(2020)01-0144-05

作者:

张家艳¹ ; 郑建立¹ ; 郑西川² ; 夏 涛¹

1. 上海理工大学,上海 200093; 2. 上海交通大学,上海 200233

Author(s):

ZHANG Jia-yan ¹ ; ZHENG Jian-li ¹ ; ZHENG Xi-chuan ² ; XIA Tao ¹

1. University of Shanghai for Science and Technology,Shanghai 200093,China; 2. Shanghai Jiaotong University,Shanghai 200233,China

关键词:

MIMIC 数据库; 人工智能; 机器学习; 深度学习; 数据挖掘; 医疗质量

Keywords:

MIMIC database; artificial intelligence; machine learning; deep learning; data mining; medical quality

分类号:

TP392

DOI:

10. 3969 / j. issn. 1673-629X. 2020. 01. 026

摘要:

开源数据库-重症特别护理信息集 MIMIC 数据库包含了大量的医学数据,自它发布之日起,便得到了众多研究人员的青睐。 但低效的挖掘方法很难发现内部的隐含信息,这使得 MIMIC 数据库得不到很好的利用,造成了资源的浪费。探索新兴的挖掘方法进行知识发现便显得异常重要。 文中对围绕 MIMIC 数据库的各种挖掘方法进行综述,重点阐述了新出现的机器学习和深度学习方法。 同时将传统统计学模型与新出现的人工智能技术包括机器学习和深度学习技术进行比较分析。 结果发现相比传统的统计学模型,机器学习和深度学习技术在预测病人的早期死亡率、发现疾病影响因素等方面普遍效果更好,这有助于改善医疗质量、帮助医生进行辅助诊断,在一定程度上也减少了病人的医疗费用。

Abstract:

The open source database,the medical information mart for intensive care (MIMIC),which contains a large amount of medical data,has been favored by many researchers since its release. However,inefficient mining methods are difficult to find internal hidden information,which makes the MIMIC database not well utilized and causes resource waste. It is extremely important to explore emerging mining methods for knowledge discovery. We summarize the various mining methods around the MIMIC database,focusing on emerging machine learning and deep learning methods. At the same time,the traditional statistical model is compared with the emerging artificialintelligence technologies including machine learning and deep learning. It was found that machine learning and deep learning generally perform better in predicting early mortality and finding factors affecting the disease than traditional statistical models,which helps improve the quality of medical care,assist doctors in diagnosis,and reduce the cost of medical care for patients to some extent.

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更新日期/Last Update: 2020-01-10