BACKGROUND Portal vein system thrombosis (PVST) is potentially fatal for patients if the diagnosis is not timely or the treatment is not proper. There hasn't been any available technique to detect clinic risk factors to predict PVST after splenectomy in cirrhotic patients. The aim of this study is to detect the clinic risk factors of PVST for splenectomy and cardia devascularization patients for liver cirrhosis and portal hypertension, and build an efficient predictive model to PVST via the detected risk factors, by introducing the machine learning method. We collected 92 clinic indexes of splenectomy plus cardia devascularization patients for cirrhosis and portal hypertension, and proposed a novel algorithm named as RFA-PVST (Risk Factor Analysis for PVST) to detect clinic risk indexes of PVST, then built a SVM (support vector machine) predictive model via the detected risk factors. The accuracy, sensitivity, specificity, precision, F-measure, FPR (false positive rate), FNR (false negative rate), FDR (false discovery rate), AUC (area under ROC curve) and MCC (Matthews correlation coefficient) were adopted to value the predictive power of the detected risk factors. The proposed RFA-PVST algorithm was compared to mRMR, SVM-RFE, Relief, S-weight and LLEScore. The statistic test was done to verify the significance of our RFA-PVST. RESULTS Anticoagulant therapy and antiplatelet aggregation therapy are the top-2 risk clinic factors to PVST, followed by D-D (D dimer), CHOL (Cholesterol) and Ca (calcium). The SVM (support vector machine) model built on the clinic indexes including anticoagulant therapy, antiplatelet aggregation therapy, RBC (Red blood cell), D-D, CHOL, Ca, TT (thrombin time) and Weight factors has got pretty good predictive capability to PVST. It has got the highest PVST predictive accuracy of 0.89, and the best sensitivity, specificity, precision, F-measure, FNR, FPR, FDR and MCC of 1, 0.75, 0.85, 0.92, 0, 0.25, 0.15 and 0.8 respectively, and the comparable good AUC value of 0.84. The statistic test results demonstrate that there is a strong significant difference between our RFA-PVST and the compared algorithms, including mRMR, SVM-RFE, Relief, S-weight and LLEScore, that is to say, the risk indicators detected by our RFA-PVST are statistically significant. CONCLUSIONS The proposed novel RFA-PVST algorithm can detect the clinic risk factors of PVST effectively and easily. Its most contribution is that it can display all the clinic factors in a 2-dimensional space with independence and discernibility as y-axis and x-axis, respectively. Those clinic indexes in top-right corner of the 2-dimensional space are detected automatically as risk indicators. The predictive SVM model is powerful with the detected clinic risk factors of PVST. Our study can help medical doctors to make proper treatments or early diagnoses to PVST patients. This study brings the new idea to the study of clinic treatment for other diseases as well.

中文翻译：

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一种检测肝硬化和门静脉高压症的脾切除术和de门血运重建术患者门静脉系统血栓形成的关键临床因素的新方法。

背景技术如果诊断不及时或治疗不当，门静脉系统血栓形成（PVST）可能对患者致命。尚无任何可用于检测临床危险因素的技术来预测肝硬化患者脾切除术后的PVST。这项研究的目的是检测脾切除术和de门血运重建术患者肝硬化和门静脉高压症的PVST的临床危险因素，并通过引入机器学习方法，通过检测到的危险因素建立有效的PVST预测模型。我们收集了针对肝硬化和门静脉高压症的脾切除加心脏去血管化患者的92个临床指标，并提出了一种名为RFA-PVST（PVST的危险因素分析）的新算法来检测PVST的临床风险指标，然后通过检测到的风险因素建立SVM（支持向量机）预测模型。采用了准确性，敏感性，特异性，精密度，F量度，FPR（假阳性率），FNR（假阴性率），FDR（假发现率），AUC（ROC曲线下的面积）和MCC（马修斯相关系数）评估检测到的风险因素的预测能力。将提出的RFA-PVST算法与mRMR，SVM-RFE，救济，S-weight和LLEScore进行了比较。进行统计检验以验证我们的RFA-PVST的重要性。结果抗凝治疗和抗血小板凝集治疗是PVST的前2大临床风险因素，其次是DD（D二聚体），CHOL（胆固醇）和Ca（钙）。基于临床指标的SVM（支持向量机）模型，包括抗凝治疗，抗血小板凝集治疗，RBC（红细胞），DD，CHOL，Ca，TT（凝血酶时间）和体重因子对PVST具有相当好的预测能力。PVST的预测准确度最高，为0.89，最佳灵敏度，特异性，精密度，F量度，FNR，FPR，FDR和MCC分别为1，0.75、0.85、0.92、0、0.25、0.15和0.8，并且可比的良好AUC值为0.84。统计测试结果表明，我们的RFA-PVST与所比较的算法（包括mRMR，SVM-RFE，救济，S-weight和LLEScore）之间存在很大的显着差异，也就是说，由我们的RFA-PVST检测到的风险指标PVST具有统计学意义。结论提出的新颖的RFA-PVST算法可以有效，轻松地检测PVST的临床危险因素。它的最大贡献在于，它可以在二维空间中显示所有临床因素，分别具有独立性和可分辨性，分别为y轴和x轴。二维空间右上角的那些临床指标会自动检测为风险指标。预测的SVM模型具有强大的检测到的PVST临床风险因素，因此功能强大。我们的研究可以帮助医生对PVST患者进行正确的治疗或早期诊断。这项研究也为其他疾病的临床治疗研究带来了新思路。我们的研究可以帮助医生对PVST患者进行正确的治疗或早期诊断。这项研究也为其他疾病的临床治疗研究带来了新思路。我们的研究可以帮助医生对PVST患者进行正确的治疗或早期诊断。这项研究也为其他疾病的临床治疗研究带来了新思路。