利用大型数据库早期诊断人类疾病

A. M. Al-Ansi, V. Ryabtsev, Tatyana Utkina
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引用次数: 0

摘要

本文旨在通过处理包含疾病征兆的大型数据库,展示 Eidos 智能系统早期识别人类疾病的能力。为了研究疾病征兆,建议使用在 Eidos 智能系统中实现的自动系统认知分析。自动系统认知分析从大型数据库中提取信息,并从中形成知识,从而有可能识别人类疾病。在形成模型的过程中,信息量是根据建模对象在其影响下进入与类别相对应的某种状态的因子值来计算的。这样就可以对以不同类型的测量尺度和不同的测量单位呈现的建模对象的异构观测信息进行可比和正确的处理。以下疾病的识别结果具有很高的可靠性:慢性肾病、肺癌、乳腺癌、肝病、糖尿病和中风的发病风险。由于 Eidos 系统可在互联网上免费获取,因此研究结果可应用于许多国家的医疗机构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Use of Large Databases for Diagnosing Human Diseases at Early Stage
The purpose of this article is to demonstrate the ability of the Eidos intellectual system to recognize human diseases at an early stage by processing large databases containing signs of diseases. To study the signs of diseases, it is proposed to use an automated system-cognitive analysis implemented in the Eidos intellectual system. Automated system-cognitive analysis extracts information from large databases and forms knowledge from them that makes it possible to recognize human diseases. In the process of forming models, the amount of information is calculated in the value of the factor by which the modeling object will pass under its influence to a certain state corresponding to the class. This allows for comparable and correct processing of heterogeneous information about observations of the object of modeling, presented in different types of measuring scales and different units of measurement. The results of recognition of the following diseases were obtained with high reliability: chronic kidney disease, lung cancer, breast cancer, liver disease, risks of developing diabetes and stroke. The results of the study can be applied in medical institutions in many countries, since the Eidos system is freely available on the Internet.
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