Cheng Li-Chen, Hu Ya-Han, Chiou Shr-Han
Department of Computer Science and Information Management, Soochow University, Taipei, 100, Taiwan.
Department of Information Management and Institute of Healthcare Information Management, National Chung Cheng University, Chiayi, 621, Taiwan.
J Med Syst. 2017 May;41(5):85. doi: 10.1007/s10916-017-0732-5. Epub 2017 Apr 11.
Chronic kidney disease (CKD) has attracted considerable attention in the public health domain in recent years. Researchers have exerted considerable effort in attempting to identify critical factors that may affect the deterioration of CKD. In clinical practice, the physical conditions of CKD patients are regularly recorded. The data of CKD patients are recorded as a high-dimensional time-series. Therefore, how to analyze these time-series data for identifying the factors affecting CKD deterioration becomes an interesting topic. This study aims at developing prediction models for stage 4 CKD patients to determine whether their eGFR level decreased to less than 15 ml/min/1.73m (end-stage renal disease, ESRD) 6 months after collecting their final laboratory test information by evaluating time-related features. A total of 463 CKD patients collected from January 2004 to December 2013 at one of the biggest dialysis centers in southern Taiwan were included in the experimental evaluation. We integrated the temporal abstraction (TA) technique with data mining methods to develop CKD progression prediction models. Specifically, the TA technique was used to extract vital features (TA-related features) from high-dimensional time-series data, after which several data mining techniques, including C4.5, classification and regression tree (CART), support vector machine, and adaptive boosting (AdaBoost), were applied to develop CKD progression prediction models. The results revealed that incorporating temporal information into the prediction models increased the efficiency of the models. The AdaBoost+CART model exhibited the most accurate prediction among the constructed models (Accuracy: 0.662, Sensitivity: 0.620, Specificity: 0.704, and AUC: 0.715). A number of TA-related features were found to be associated with the deterioration of renal function. These features can provide further clinical information to explain the progression of CKD. TA-related features extracted by long-term tracking of changes in laboratory test values can enable early diagnosis of ESRD. The developed models using these features can facilitate medical personnel in making clinical decisions to provide appropriate diagnoses and improved care quality to patients with CKD.
近年来，慢性肾脏病（CKD）在公共卫生领域备受关注。研究人员在试图确定可能影响CKD恶化的关键因素方面付出了巨大努力。在临床实践中，CKD患者的身体状况会定期记录。CKD患者的数据被记录为高维时间序列。因此，如何分析这些时间序列数据以识别影响CKD恶化的因素成为一个有趣的话题。本研究旨在通过评估与时间相关的特征，为4期CKD患者开发预测模型，以确定在收集其最终实验室检查信息6个月后，他们的估算肾小球滤过率（eGFR）水平是否降至低于15 ml/min/1.73m²（终末期肾病，ESRD）。实验评估纳入了2004年1月至2013年12月在台湾南部最大的透析中心之一收集的463例CKD患者。我们将时间抽象（TA）技术与数据挖掘方法相结合，以开发CKD进展预测模型。具体而言，TA技术用于从高维时间序列数据中提取重要特征（与TA相关的特征），之后应用包括C4.5、分类与回归树（CART）、支持向量机和自适应增强（AdaBoost）在内的多种数据挖掘技术来开发CKD进展预测模型。结果表明，将时间信息纳入预测模型提高了模型的效率。在构建的模型中，AdaBoost + CART模型表现出最准确的预测（准确率：0.662，灵敏度：0.620，特异性：0.704，曲线下面积：0.715）。发现许多与TA相关的特征与肾功能恶化有关。这些特征可以提供进一步的临床信息来解释CKD的进展。通过长期跟踪实验室检查值的变化提取的与TA相关的特征能够实现ESRD的早期诊断。使用这些特征开发的模型可以帮助医务人员做出临床决策，为CKD患者提供适当的诊断并提高护理质量。
