@Article{信息:doi 10.2196 / / medinform。9960，作者=“alex - brosou, Stephane and Kim, James and Li, Li and Liu, Hui”，标题=“预测客户投诉原因:用机器学习预测体外诊断分析质量问题的第一步”，期刊=“JMIR Med Inform”，年=“2018”，月=“5”，日=“15”，卷=“6”，数=“2”，页数=“e34”，关键词=“后市场监测;QC化学结果;投诉数据;车;背景:医疗保健行业的供应商生产的诊断系统，通过安全连接，允许他们几乎实时地监控性能。然而，在分析和解释大量有噪声的质量控制(QC)数据方面存在挑战。因此，一些QC轮班可能没有被供应商及时发现，但导致客户投诉。目的:本研究的目的是假设可以更有效地利用收集的QC数据来设计更积极主动的响应。因此，我们的目标是通过体外诊断系统收集的QC数据来预测客户的投诉。 Methods: QC data from five select in vitro diagnostic assays were combined with the corresponding database of customer complaints over a period of 90 days. A subset of these data over the last 45 days was also analyzed to assess how the length of the training period affects predictions. We defined a set of features used to train two classifiers, one based on decision trees and the other based on adaptive boosting, and assessed model performance by cross-validation. Results: The cross-validations showed classification error rates close to zero for some assays with adaptive boosting when predicting the potential cause of customer complaints. Performance was improved by shortening the training period when the volume of complaints increased. Denoising filters that reduced the number of categories to predict further improved performance, as their application simplified the prediction problem. Conclusions: This novel approach to predicting customer complaints based on QC data may allow the diagnostic industry, the expected end user of our approach, to proactively identify potential product quality issues and fix these before receiving customer complaints. This represents a new step in the direction of using big data toward product quality improvement. ", issn="2291-9694", doi="10.2196/medinform.9960", url="http://medinform.www.mybigtv.com/2018/2/e34/", url="https://doi.org/10.2196/medinform.9960", url="http://www.ncbi.nlm.nih.gov/pubmed/29764796" }