Algoritmi, Universidade do Minho, 4710-057, Braga, Portugal,
J Med Syst. 2015 Oct;39(10):131. doi: 10.1007/s10916-015-0313-4. Epub 2015 Aug 27.
Kidney renal failure means that one's kidney have unexpectedly stopped functioning, i.e., once chronic disease is exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient's history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapid deterioration of the renal function, but is often reversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis. The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow one to consider incomplete, unknown, and even contradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9-94.2 %, respectively.
肾衰意味着肾脏突然停止工作，即一旦出现慢性疾病，必须评估肾脏功能障碍的存在或程度及其进展情况，并诊断潜在的综合征。虽然患者的病史和体格检查可能表示良好的实践，但一些关键信息必须通过评估肾小球滤过率和分析血清生物标志物来获得。事实上，慢性肾脏病表现为异常的肾功能和/或其组成，即有证据表明，通过减少和预防某些相关并发症的发生，如高血压、肥胖、糖尿病和心血管并发症，可以避免或延缓其进展，从而进行治疗。急性肾损伤突然出现，肾功能迅速恶化，但如果早期发现并及时治疗，通常是可以逆转的。在这两种情况下，即急性肾损伤和慢性肾脏病，早期干预可以显著改善预后。因此，尽管使用传统方法和现有的问题解决工具进行评估很困难，但评估这些病理情况是强制性的。在这项工作中，我们将专注于开发一种混合决策支持系统，从基于逻辑编程的知识表示和推理过程方面，使其能够考虑不完整、未知甚至矛盾的信息，并结合一种以人工神经网络为中心的计算方法，以衡量对这种情况的置信度。本研究涉及 558 名年龄平均为 51.7 岁的患者，其中 175 例观察到慢性肾脏病。该数据集包含 24 个变量，分为五个主要类别。所提出的模型在慢性肾脏病的诊断中表现出良好的性能，因为敏感性和特异性表现出的数值范围在 93.1%到 94.9%和 91.9%到 94.2%之间。
