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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (1): 88-98    DOI: 10.11925/infotech.2096-3467.2017.1053
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Reducing Data Dimension of Electronic Medical Records: An Empirical Study
Mu Dongmei(), Wang Ping, Zhao Danning
School of Public Health, Jilin University, Changchun 130021, China
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Abstract  

[Objective] This paper explores the strategy of reducing the data dimension of electronic medical records, aiming to improve the knowledge discovery. [Methods] First, we conducted preliminary dimension reduction through literature review. Then, we used three methods to finish the second round of dimension reduction. We extracted the factors with the eigenvalue greater than 1, with the cumulative contribution rate greater than 85%, as well as factors of significant differences. Finally, we compared results of the three methods with empirical research. [Results] The dimensional reduction methods extracted 8, 17 and 14 attributes respectively. After qualitative and quantitative evaluation, the principal component analysis method yielded the best result, whose dimension of the feature root was larger than 1. [Limitations] The sample size needs to be expanded for more in-depth analysis. [Conclusions] The proposed method could effectively reduce the data dimension of electronic medical records.
Key words： Dimension Reduction      Data Mining      Knowledge Discovery      Electronic Medical Record     
Received: 23 October 2017      Published: 05 February 2018
ZTFLH:  G353.1  
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Dongmei Mu
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Danning Zhao

Cite this article:

Mu Dongmei,Wang Ping,Zhao Danning. Reducing Data Dimension of Electronic Medical Records: An Empirical Study. Data Analysis and Knowledge Discovery, 2018, 2(1): 88-98.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.1053     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I1/88

表格类型 表格数 数据量 属性简介
诊断表 1 16 508 patient ID、visit ID、diagnosis、诊断时间、诊断内容、治疗时间、治疗结果
基本信息表 1 10 791 patient ID、birth place、data of birth、sex
病人ID表 2 10 791 patient ID
生理指标表 1 65 536 patient ID、visit ID、record data、体重、体温、脉搏、呼吸、血压
生化指标表 12 45 803 主题词、申请序号、病人ID号、性别、诊断、检验时间、费用类型、血液样本类型、样本记录说明、检查时间及各种生化指标: 总胆固醇、高密度脂蛋白、低密度脂蛋白、甘油三酯、乳酸脱氢酶、载脂蛋白A、载脂蛋白B等
用药表 1 65 536 病人ID号、药品名称、起止时间、用法与用量、用药频次、费用等
糖化指标表 12 5 230 主题词(全血糖化血红蛋白)、申请序号、病人ID、性别、年龄、费别、血液样本类型、标本说明、时间、全血糖化血红蛋白测定量
序号 主成分 英文缩写 旋转后因子载荷 特征根
1 直接胆红素 DBIL 0.959 2.743
2 钙 Ca 0.820 2.563
3 钠 Na 0.857 2.339
4 载脂蛋白B apoB 0.786 2.190
5 总胆固醇 TC 0.866 1.826
6 钾 K 0.686 1.738
7 全血糖化血红蛋白 Hb 0.790 1.507
8 高密度脂蛋白 HDL 0.731 1.119
序号 主成分 英文缩写 旋转后
因子载荷		 累计贡献率
1 直接胆红素 DBIL 0.961 10.158
2 血清白蛋白 ALB 0.873 19.650
3 总胆固醇 TC 0.944 28.314
4 氯化物 Cl 0.914 36.427
5 甘油三酯 TG 0.890 43.191
6 全血糖化血红蛋白 Hb 0.834 49.627
7 载脂蛋白A1 apoA1 0.786 55.207
8 年龄 age 0.870 59.350
9 二氧化碳 CO2 0.912 63.033
10 钾 K 0.952 66.525
11 游离钙 f-Ca 0.964 69.908
12 血清尿酸 SUA 0.942 73.159
13 性别 sex 0.888 76.242
14 镁 Mg 0.971 79.214
15 乳酸脱氢酶 LDH 0.972 81.920
16 血清脂蛋白 LP(a) 0.973 84.609
17 糖化血清蛋白 GSP 0.934 87.023
序号 属性 B sig. Exp(B)
1 葡萄糖(GS) 0.103 0.000 1.108
2 总胆固醇(TC) 0.441 0.000 1.554
3 乳酸脱氢酶(LDH) 0.005 0.000 1.005
4 低密度脂蛋白(LDL) -0.544 0.000 0.580
5 全血糖化血红蛋白(Hb) -0.128 0.005 0.880
6 总胆红素(STB) -0.030 0.008 0.970
7 直接胆红素(DBIL) 0.246 0.000 1.279
8 总胆汁酸(TBA) 0.048 0.004 1.049
9 钾(K) -0.670 0.000 0.512
10 氯化物(Cl) -0.105 0.000 0.901
11 无机磷(P) -1.215 0.000 2.97
12 镁(Mg) 2.948 0.000 19.071
13 游离钙(f-Ca) 3.009 0.035 20.265
14 二氧化碳(CO2) -0.176 0.000 0.839
项目 降维方法1 降维方法2 降维方法3
降维原理 特征向量
特征根		 特征向量累
计贡献率		 特征向量患病
差异显著性
降维方法 主成分分析 主成分分析 逻辑回归
降维条件 特征根大于1 累计贡献率大于85% P值小于0.05
提取因子数量 8个 17个 14个
提取因子百分比 29.63% 62.96% 51.85%
方法 TP FP Precision Recall F-Measure ROC Area Kappa
未降维 0.935 0.096 0.935 0.935 0.935 0.925 0.838
降维方法1 0.975 0.045 0.974 0.975 0.974 0.975 0.936
降维方法2 0.933 0.093 0.934 0.933 0.934 0.925 0.836
降维方法3 0.941 0.088 0.941 0.941 0.941 0.937 0.879
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