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Machine-learning based query construction and pattern identification for hereditary angioedema

机译:基于机器学习的遗传性血管水肿查询构造与模式识别

摘要

A method, computer program product, and system identifying a probability of a medical condition in a patient. The method includes a processor obtaining data set(s) related to a patient population diagnosed with a medical condition and based on a frequency of features in the data set(s), identifying common features and weighting the common features based on frequency of occurrence in the data set(s) to generate mutual information. The processor generates pattern(s) including a portion of the common features to generate a machine learning algorithm(s). The processor compiles a training set of data to use to tune the machine learning algorithm(s). The processor dynamically adjusts common features in the pattern(s) such that the machine learning algorithm(s) can distinguish patient data indicating the medical condition from patient data not indicating the medical condition. The processor applies the machine learning algorithm(s) to data related to the undiagnosed patient, to determine the probability.
机译:一种方法、计算机程序产品和系统,用于识别患者身体状况的可能性。该方法包括处理器,其基于数据集中的特征频率,获取与被诊断患有疾病的患者群体相关的数据集,识别公共特征,并基于数据集中的出现频率对公共特征进行加权,以生成互信息。处理器生成包括一部分公共特征的模式,以生成机器学习算法。处理器编译一组训练数据,用于调整机器学习算法。处理器动态调整模式中的公共特征,以便机器学习算法能够区分指示医疗状况的患者数据和不指示医疗状况的患者数据。处理器将机器学习算法应用于与未诊断患者相关的数据,以确定概率。

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