Abstract
BackgroundChronic 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.MethodsWe 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.ResultsAll 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.ConclusionsThis ANN approach may allow for a more efficient risk adapted HCV-screening. However, further validation of the prediction model is required.
Highlights
Chronic hepatitis C virus (HCV)-infection is a slowly debilitating and potentially fatal disease with a high estimated number of undiagnosed cases
With the recent introduction of several direct-acting antivirals (DAAs) treatment of chronic HCV infection has become highly effective with cure rates approaching 100% [6]
No variables directly linked to the diagnosis of HCV were chosen to train the artificial neural network (ANN)
Summary
Chronic hepatitis C virus (HCV)-infection is a slowly debilitating and potentially fatal disease with a high estimated number of undiagnosed cases. An estimated 70 million people are chronically infected with the hepatitis C virus of whom only 20% are believed to be diagnosed [3]. The cumulative number of patients who have received treatment over the years reached 5.4 million in 2015. Most of these patients received older, interferon-based therapies characterized by substantial side effects and lower sustained elimination rates approximating 50% [5]. With the recent introduction of several direct-acting antivirals (DAAs) treatment of chronic HCV infection has become highly effective with cure rates approaching 100% [6]. The excellent safety and low side effect profiles of DAAs make treatment even of advanced disease states feasible
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