根据神经网络风险因素分析,诊断和预测胆固醇的可能性

Виктор Анатольевич Лазаренко, Андрей Евгеньевич Антонов
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引用次数: 2

摘要

目的。通过对危险因素数据的分析,开发用于胆囊炎诊断和预测的人工神经网络,并探讨其在实际临床应用的可能性。材料和方法。资料收集工作在库尔斯克市的医院进行,其中包括对488名肝胰十二指肠疾病患者的调查。203例患者发生胆囊炎,285例患者排除胆囊炎诊断。使用内部开发的人工神经网络(以双曲正切为激活函数的多层感知器)对风险因素数据(如性别、年龄、不良习惯、职业、家庭关系等)进行分析。计算机程序“疾病智力分析诊断系统”按既定程序注册(证书编号:2017613090)。结果。使用神经网络分析风险因素的数据,与形成临床图像的信息处理相比较,可以在症状出现之前诊断出胆囊炎的潜在疾病。对人工神经网络进行训练,并对可能住院的年龄进行定量输出编码,从而可以生成一系列与经验数据显著(α≤0.001)相同的值。训练组的计算平均值与经验平均值的差值为0.45,临床批准组的差值为1.75。平均绝对误差在1.87 ~ 2.07年之间。结论:1。提出的新方法对胆囊炎的诊断和预后有一定的疗效,其敏感性(94.44%,m = 2.26)和特异性(80.6%,m = 3.9)在临床批准中得到了证实。2. 预测胆囊炎患者可能住院年龄的误差分别不超过2.29岁和2.38岁,p = 0.95和p = 0.99。
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Диагностика и прогнозирование вероятности возникновения холецистита на основе нейросетевого анализа факторов риска
Purpose. To develop an artificial neural network for diagnosing and predicting the development of cholecystitis based on an analysis of data on risk factors, and to explore the possibilities of its application in real clinical practice. Materials and methods. The collection of materials was held in at the hospitals of the city of Kursk and included a survey of 488 patients with hepatopancreatoduodenal diseases. 203 patients were suffering from cholecystitis, in 285 patients the diagnosis of cholecystitis was excluded. Analysis of risk factors’ data (such as sex, age, bad habits, profession, family relationships, etc.) was carried out using an internally developed artificial neural network (multilayer perceptron with hyperbolic tangent as the activation function). The computer program “System of Intellectual Analysis and Diagnosis of Diseases” was registered in accordance with established procedure (Certificate No. 2017613090). Results. The use of neural network analysis of data on risk factors in comparison with the processing of information that forms a clinical picture allows the diagnosis of a potential disease with cholecystitis before the onset of symptoms. The training of the artificial neural network with a quantitative output coding the age of probable hospitalization made it possible to generate an array of values, signifficantly (α ≤ 0.001) not differing from the empirical data. The difference between the mean calculated and mean empirical values was 0.45 for the training set and 1.75 for the clinical approbation group. The mean absolute error was within the range of 1.87–2.07 years. Conclusion. 1. The proposed new approach to the diagnosis and prognosis of cholecystitis has demonstrated its effectiveness, which is confirmed in clinical approbation by the levels of sensitivity (94.44%, m = 2.26) and specificity (80.6%, m = 3.9). 2. The error in predicting the age of probable hospitalization of patients with cholecystitis did not exceed 2.29 and 2.38 years for p = 0.95 and p = 0.99, respectively.
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