Dept. of Internal Medicine & Gastroenterlogy, Klinikum Vest GmbH - Paracelsus-Klinik Marl, Lipper Weg 11, 45772, Marl, Germany.
Deutsche Leberstiftung (German Liver Foundation), Carl-Neuberg-Str. 1, 30625, Hannover, Germany.
J Transl Med. 2019 Mar 19;17(1):94. doi: 10.1186/s12967-019-1832-4.
Chronic hepatitis C virus (HCV)-infection is a slowly debilitating and potentially fatal disease with a high estimated number of undiagnosed cases. Given the major advances in the treatment, detection of unreported infections is a consequential step for eliminating hepatitis C on a population basis. The prevalence of chronic hepatitis C is, however, low in most countries making mass screening neither cost effective nor practicable.
We used a Kohonen artificial neural network (ANN) to analyze socio-medical data of 1.8 million insurants for predictors of undiagnosed HCV infections. The data had to be anonymized due to ethical requirements. The network was trained with variables obtained from a subgroup of 2544 patients with confirmed hepatitis C-virus (HCV) infections excluding variables directly linked to the diagnosis of HCV. All analyses were performed using the data mining solution "RayQ". Training results were visualized three-dimensionally and the distributions and characteristics of the clusters were explored within the map.
All 2544 patients with confirmed chronic HCV diagnoses were localized in a clearly defined cluster within the Kohonen self-organizing map. An additional 2217 patients who had not been diagnosed with hepatitis C co-localized to the same cluster, indicating socio-medical similarities and a potentially elevated risk of infection. Several factors including, age, diagnosis codes and drug prescriptions acted only in conjunction as predictors of an elevated HCV risk.
This ANN approach may allow for a more efficient risk adapted HCV-screening. However, further validation of the prediction model is required.
慢性丙型肝炎病毒（HCV）感染是一种缓慢使人虚弱且可能致命的疾病，估计有大量未确诊的病例。鉴于治疗方面的重大进展，发现未报告的感染是在人群基础上消除丙型肝炎的重要步骤。然而，在大多数国家，慢性丙型肝炎的患病率较低，因此大规模筛查既不具有成本效益，也不切实际。
我们使用科恩恩人工神经网络（ANN）分析了 180 万被保险人的社会医学数据，以寻找未确诊 HCV 感染的预测因素。由于伦理要求，数据必须进行匿名化处理。该网络使用从 2544 名确诊丙型肝炎病毒（HCV）感染患者的亚组中获得的变量进行训练，排除与 HCV 诊断直接相关的变量。所有分析均使用数据挖掘解决方案“RayQ”进行。训练结果以三维形式可视化，并在地图内探索聚类的分布和特征。
所有 2544 名确诊慢性 HCV 诊断的患者均位于科恩恩自组织映射中的一个明确界定的聚类中。另外 2217 名未被诊断患有丙型肝炎的患者也共同聚集在同一个聚类中，表明存在社会医学相似性和潜在的感染风险增加。包括年龄、诊断代码和药物处方在内的多个因素仅作为 HCV 风险升高的预测因素共同作用。
这种 ANN 方法可能允许更有效地适应风险的 HCV 筛查。然而，需要进一步验证预测模型。
