Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan.
PLoS One. 2012;7(12):e51285. doi: 10.1371/journal.pone.0051285. Epub 2012 Dec 28.
Few studies of laparoscopic cholecystectomy (LC) outcome have used longitudinal data for more than two years. Moreover, no studies have considered group differences in factors other than outcome such as age and nonsurgical treatment. Additionally, almost all published articles agree that the essential issue of the internal validity (reproducibility) of the artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR) and multiple linear regression (MLR) models has not been adequately addressed. This study proposed to validate the use of these models for predicting quality of life (QOL) after LC and to compare the predictive capability of ANNs with that of SVM, GPR and MLR.
METHODOLOGY/PRINCIPAL FINDINGS: A total of 400 LC patients completed the SF-36 and the Gastrointestinal Quality of Life Index at baseline and at 2 years postoperatively. The criteria for evaluating the accuracy of the system models were mean square error (MSE) and mean absolute percentage error (MAPE). A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and to rank the variables in order of importance. Compared to SVM, GPR and MLR models, the ANN model generally had smaller MSE and MAPE values in the training data set and test data set. Most ANN models had MAPE values ranging from 4.20% to 8.60%, and most had high prediction accuracy. The global sensitivity analysis also showed that preoperative functional status was the best parameter for predicting QOL after LC.
CONCLUSIONS/SIGNIFICANCE: Compared with SVM, GPR and MLR models, the ANN model in this study was more accurate in predicting patient-reported QOL and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data.
很少有研究使用超过两年的纵向数据来研究腹腔镜胆囊切除术（LC）的结果。此外，没有研究考虑除结果以外的因素（如年龄和非手术治疗）的组间差异。此外，几乎所有已发表的文章都认为，人工神经网络（ANN）、支持向量机（SVM）、高斯过程回归（GPR）和多元线性回归（MLR）模型的内部有效性（可重复性）的基本问题尚未得到充分解决。本研究旨在验证这些模型在预测 LC 后生活质量（QOL）方面的应用，并比较 ANN 与 SVM、GPR 和 MLR 的预测能力。
方法/主要发现：共有 400 例 LC 患者在基线和术后 2 年时完成了 SF-36 和胃肠道生活质量指数（GIQLI）的测定。评估系统模型准确性的标准为均方误差（MSE）和平均绝对百分比误差（MAPE）。还进行了全局灵敏度分析，以评估系统模型中输入参数的相对重要性，并按重要性对变量进行排序。与 SVM、GPR 和 MLR 模型相比，ANN 模型在训练数据集和测试数据集中通常具有较小的 MSE 和 MAPE 值。大多数 ANN 模型的 MAPE 值范围为 4.20%至 8.60%，且具有较高的预测准确性。全局灵敏度分析还表明，术前功能状态是预测 LC 后 QOL 的最佳参数。
结论/意义：与 SVM、GPR 和 MLR 模型相比，本研究中的 ANN 模型在预测患者报告的 QOL 方面更准确，整体性能指标更高。对该模型的进一步研究可能需要考虑更详细的数据库，包括并发症和临床检查结果以及更详细的结果数据的影响。
