Building Disease Prediction Model Using Support Vector Machine ——Case Study of Severe Acute Pancreatitis
Zhang Ye1,Zhang Han1,Yin Bincan1,Zhao Yuhong2()
1Department of Medical Informatics, China Medical University, Shenyang 110122, China 2Shengjing Hospital of China Medical University, Shenyang 110004, China
[Objective] This study developed a disease prediction model based on the support vector machine, using electronic medical records of the severe acute pancreatitis patients. [Methods] We first adjusted the kernel type and parameter values of the support vector machine method to get an optimized prediction model. Then, we combined it with univariable and multivariable logistic regression analysis methods to select features’ variable. Finally, we proposed a simplified early warning model for the severe acute pancreatitis. [Results] The new model’s prediction accuracy rate is 70.37%. Variables used by this model include: white blood cell count, serum calcium, serum lipase, systolic blood pressure, diastolic blood pressure and pleural effusion. [Limitations] Because of the small sample size, we only used this support vector machine method to develop the new disease prediction model. In the future, we will try to establish a larger examination system for the clinical trial. [Conclusions] Support vector machine can help us develop an optimal disease prediction model. A new system based on this model could support our clinical decision makings.
张晔,张晗,尹玢璨,赵玉虹. 基于电子病历利用支持向量机构建疾病预测模型*——以重度急性胰腺炎早期预警为例[J]. 现代图书情报技术, 2016, 32(2): 83-89.
Zhang Ye,Zhang Han,Yin Bincan,Zhao Yuhong. Building Disease Prediction Model Using Support Vector Machine ——Case Study of Severe Acute Pancreatitis. New Technology of Library and Information Service, 2016, 32(2): 83-89.
(Lei Jianbo.Clinical Decision Support and the Core Value of Electronic Medical Record[J]. China Digital Medicine, 2008, 3(3): 26-30.)
[2]
Byrd R J, Steinhubl S R, Sun J, et al.Automatic Identification of Heart Failure Diagnostic Criteria, Using Text Analysis of Clinical Notes from Electronic Health Records[J]. International Journal of Medical Informatics, 2014, 83(12): 983-992.
[3]
Ye J, Farnum M, Yang E, et al.Sparse Learning and Stability Selection for Predicting MCI to AD Conversion Using Baseline ADNI Data[J]. BMC Neurology, 2012. DOI: 10.1186/1471-2377-12-46.
[4]
Alvarez C A, Clark C A, Zhang S, et al.Predicting out of Intensive Care Unit Cardiopulmonary Arrest or Death Using Electronic Medical Record Data[J]. BMC Medical Informatics and Decision Making, 2013. DOI: 10.1186/1472- 6947-13-128.
[5]
Matheny M E, Fitzhenry F, Speroff T, et al.Detection of Infectious Symptoms from VA Emergency Department and Primary Care Clinical Documentation[J]. International Journal of Medical Informatics, 2012, 81(3): 143-156.
[6]
Kim S Y, Moon S K, Jung D C, et al.Pre-Operative Prediction of Advanced Prostatic Cancer Using Clinical Decision Support Systems: Accuracy Comparison between Support Vector Machine and Artificial Neural Network[J]. Korean Journal of Radiology, 2011, 12(5): 588-594.
[7]
Kim W, Kim K S, Lee J E, et al.Development of Novel Breast Cancer Recurrence Prediction Model Using Support Vector Machine[J]. Journal of Breast Cancer, 2012, 15(2): 230-238.
(Lv Yi, Wang Qing.A Probability Calibration and Ensemble Learning Based Colorectal Cancer Liver Metastasis Prediction Model[J]. Computer Applications and Software, 2011, 28(9): 48-51.)
[9]
王星, 等. 大数据分析: 方法与应用[M]. 北京: 清华大学出版社, 2013: 68-90.
[9]
(Wang Xing, et al.Big Data Analysis: Methods and Applications[M]. Beijing: Tsinghua University Press, 2013: 68-90.)
[10]
陈永义, 熊秋芬. 支持向量机方法应用教程[M]. 北京: 气象出版社, 2011: 6-10.
[10]
(Chen Yongyi, Xiong Qiufen.Application of Support Vector Machines Tutorial [M]. Beijing: China Meteorological Press, 2011: 6-10.)
[11]
ICTCLAS 2014 [EB/OL]. [2015-03-25]. .
[12]
LIBSVM—A Library for Support Vector Machines [EB/OL]. [2015-03-25]. .
[13]
Up To Data [EB/OL]. [2015-03-25]. .
[14]
Banks P A, Bollen T L, Dervenis C, et al.Classification of Acute Pancreatitis--2012: Revision of the Atlanta Classification and Definitions by International Consensus[J]. Gut, 2013, 62(1): 102-111.
(Liu Kan, Zhu Huaiping, Liu Xiuqin.Detection of Internet Deceptive Opinion Based on SVM[J]. New Technology of Library and Information Service, 2013(11): 75-80.)