Department of Computer Engineering, Yazd University, Yazd, Iran.
Artif Intell Med. 2019 Mar;94:79-87. doi: 10.1016/j.artmed.2019.01.004. Epub 2019 Jan 23.
Since a significant number of frequent laboratory blood tests are unnecessary and these tests may have complications, developing a system that could identify unnecessary tests is essential. In this paper, a value prediction approach is presented to predict the values of Calcium and Hematocrit laboratory blood tests for upper gastrointestinal bleeding patients and patients with unspecified hemorrhage in their gastrointestinal tract. The data have been extracted from the MIMIC-II database. By considering the issues of MIMIC-II in the process of data extraction and using expert knowledge, comprehensive preprocessing has been performed to validate the data. The first prediction system is developed using zero order Takagi-Sugeno fuzzy modeling and the sequential forward selection method. The results of this prediction system for target laboratory tests are promising. In the second proposed prediction system, patients are clustered using their comorbidity information before the final prediction phase. For each cluster, a medication feature is constructed and added to the data for the final feature selection. Although it was expected that clustering patients based on comorbidity data could improve the results of value prediction, the results were not improved in average. The reason for this could be the small number of abnormal laboratory test samples and their dispersion in clusters. These abnormal values would be more dispersed in the model with clustering phase, when they are scattered over different clusters.
由于大量频繁的实验室血液检测是不必要的，并且这些检测可能会带来并发症，因此开发一种能够识别不必要检测的系统是至关重要的。在本文中，提出了一种值预测方法，用于预测上消化道出血患者和胃肠道不明出血患者的血钙和血细胞比容实验室血液检测值。数据取自 MIMIC-II 数据库。通过在数据提取过程中考虑 MIMIC-II 的问题，并利用专家知识，对数据进行了全面的预处理，以验证数据。第一个预测系统是使用零阶 Takagi-Sugeno 模糊建模和顺序前向选择方法开发的。该预测系统对目标实验室检测的结果很有前景。在第二个提出的预测系统中，在最终预测阶段之前，根据患者的合并症信息对患者进行聚类。对于每个聚类，构建一个药物特征并添加到数据中进行最终特征选择。虽然预期基于合并症数据对患者进行聚类可以提高值预测的结果，但平均而言，结果并没有得到改善。造成这种情况的原因可能是异常实验室检测样本数量较少且在聚类中分散。当这些异常值分散在不同的聚类中时，它们在具有聚类阶段的模型中会更加分散。
