Chien Ching-Wen, Lee Yi-Chih, Ma Tsochiang, Lee Tian-Shyug, Lin Yang-Chu, Wang Weu, Lee Wei-Jei
Institute of Hospital and Health Care Administration, National Yang-Ming University, Taipei, Taiwan.
Hepatogastroenterology. 2008 May-Jun;55(84):1140-5.
BACKGROUND/AIMS: Gastric cancer remains a leading cause of death worldwide. Post-operative complication is one important factor which causes mortality of gastric cancer patients after gastrectomy. Better prediction of post-operative complication before gastrectomy can significantly reduce post-operative mortality and morbidity. Therefore, 3 data mining techniques were applied in this study on improving prediction of post-operative complication.
A retrospective study was performed on 521 patients from 3 over 2,000 acute-bed medical centers in Taiwan during February 2002 to October 2004. Pre- and post-operative clinical data were collected and analyzed by applying 3 data mining techniques, included Artificial Neural Networks (ANN), Decision Tree (DT) and Logistic Regression (LR).
Results of this study indicated that ANN was a better technique than DT and LR in predicting post-operative complication. Nutritious status, pathological characteristics and operational characteristics were important predictors of post-operative complication.
Further study on predicting postoperative complication in gastric cancer patients is still important. However, how to combine different data mining techniques to improve accuracies of prediction will be another important issue for clinicians and researchers.
背景/目的：胃癌仍是全球主要的死亡原因之一。术后并发症是导致胃癌患者胃切除术后死亡的一个重要因素。在胃切除术前更好地预测术后并发症可显著降低术后死亡率和发病率。因此，本研究应用三种数据挖掘技术来改善对术后并发症的预测。
对2002年2月至2004年10月期间台湾3家拥有2000多张急性病床的医疗中心的521例患者进行了一项回顾性研究。收集术前和术后临床数据，并应用三种数据挖掘技术进行分析，包括人工神经网络（ANN）、决策树（DT）和逻辑回归（LR）。
本研究结果表明，在预测术后并发症方面，人工神经网络是比决策树和逻辑回归更好的技术。营养状况、病理特征和手术特征是术后并发症的重要预测因素。
对胃癌患者术后并发症预测的进一步研究仍然很重要。然而，如何结合不同的数据挖掘技术来提高预测准确性将是临床医生和研究人员面临的另一个重要问题。
