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SYSTEM AND METHODS FOR AN ARTIFICIAL INTELLIGENCE (AI) BASED APPROACH FOR PREDICTIVE MEDICATION ADHERENCE INDEX (MAI)

机译:基于人工智能(AI)的预测药物依从性指数(MAI)的系统和方法

摘要

A method for training an adherence model, the method including: extracting data for a group of individuals (510), wherein the extracted data includes demographic data (205) and clinical data (210); training a linear regression model (520) using a set of hyperparameter pairs (L1, Alpha) (515), wherein the linear regression model produces an adherence index based upon the extracted data, further including: for each hyperparameter pair (L1, Alpha) in the set of hyperparameter pairs, training the linear regression model using a training data set to produce a linear regression model for each hyperparameter pair (L1, Alpha) and calculating a performance metric R2 for the resulting model based upon a validation data set (525), wherein the training data set is a subset of the extracted data and the validation data set is a subset of the extracted data that is different from the training data set; and identifying the linear regression model with the largest performance metric R2 (530).
机译:一种用于训练依从性模型的方法,该方法包括:提取一组个人的数据(510),其中所提取的数据包括人口统计数据(205)和临床数据(210);以及使用一组超参数对(L1,Alpha)训练线性回归模型(520)(515),其中线性回归模型基于提取的数据生成依从性指标,还包括:对于每个超参数对(L1,Alpha)在一组超参数对中,使用训练数据集对线性回归模型进行训练以为每个超参数对(L1,Alpha)生成线性回归模型,并基于验证数据集为所得模型计算性能指标R2(525 ),其中训练数据集是提取的数据的子集,而验证数据集是提取的数据的与训练数据集不同的子集;并确定具有最大性能指标R2的线性回归模型(530)。

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