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Preprocessing Algorithms of Fuzzy Medical Data of Patient Examinations

机译：病人检查的模糊医学数据的预处理算法

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This research work proposes a novel algorithms of processing fuzzy data got while medical examinations of patients in the hospital. These algorithms are based on the use of a set of binary masks of a set of examination parameter states, classified by parameter sets for each type of medical examination. The amount of medical examinations for patient in a hospital differs by a more comprehensive set of examinations. And the amount of fuzzy data generated is significantly higher than in outpatient diagnostics. To solve problems associated with getting a clinical diagnosis, predicting the dynamics of a patient's clinical condition and the outcome of a patient's treatment based on medical screenings and examinations the specific algorithms are necessary both at the initial stage and during treatment. These algorithms are coding / decoding, classification and building of structured interconnected models and states of the diagnostic system throughout the entire patient treatment process. A diagnostic system is a set of methods, knowledge and algorithms for solving the problem of decision support in the clinical diagnostics of patients' diseases in a hospital or outpatient clinic. The methods and algorithms presented in the article allow pre-processing fuzzy data of medical examinations of patients in order to optimize and further use fuzzy cognitive models and artificial intelligence methods. Also a method for the classification of medical examination parameters is proposed for applying the proposed algorithms.
机译：这项研究工作提出了一种新颖的处理模糊数据的算法，该模糊数据是在医院对患者进行医疗检查时得到的。这些算法基于一组检查参数状态的二进制掩码的使用，这些二进制掩码按每种医学检查的参数集分类。医院对患者进行的医疗检查的数量因一套更全面的检查而异。并且生成的模糊数据量明显高于门诊诊断中的数据量。为了解决与获得临床诊断相关的问题，基于医学检查和检查来预测患者临床状况的动态和患者治疗的结果，在初始阶段和治疗期间都需要特定的算法。这些算法是在整个患者治疗过程中对结构化的互连模型和诊断系统的状态进行编码/解码，分类和构建。诊断系统是解决医院或门诊病人疾病临床诊断中决策支持问题的一组方法，知识和算法。本文中介绍的方法和算法允许对患者的医学检查进行模糊数据预处理，以优化和进一步使用模糊认知模型和人工智能方法。还提出了一种用于医学检查参数分类的方法，以应用所提出的算法。

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