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

Building Disease Prediction Model Using Support Vector Machine——Case Study of Severe Acute Pancreatitis

【目的】为构建疾病预测模型,以重度急性胰腺炎早期预警为例,提出一种基于支持向量机的疾病预测模型构建方法。【方法】基于支持向量机LIBSVM3.11,采用优化后的径向基核函数产生的分类器,同时结合统计学单因素及多因素Logistic回归分析方法,进行特征变量选取,提出一种简单易行的重度急性胰腺炎早期预警模型。【结果】所构建重度急性胰腺炎预警模型准确率达70.37%。最终纳入模型变量包括白细胞计数、血清钙离子、血清脂肪酶、收缩压、舒张压及胸腔积液。【局限】样本量有限,主要采用支持向量机构建疾病预测模型,未来可建立系统,突出临床应用价值。【结论】支持向量机可构建疾病预测的最优模型,进一步建立系统,辅助临床决策。

[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.