用于量化病史因素如何预测疾病结果的简单进化算法

J. Camp, H. Al-Mubaid
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引用次数: 0

摘要

电子健康记录(EHR)中包含的病史信息是一个有价值的数据挖掘来源,可用于预测患者结果,从而改善治疗。本文提出了一种简单而新颖的进化算法(EA),用于识别各种病史和人口统计学因素如何预测临床结果。在这项初步研究中,我们使用有关COVID-19住院率的合成数据对EA进行了测试,结果表明EA结果比逻辑回归、神经网络或决策树结果更具信息性。
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Simple evolutionary algorithm for quantifying how medical history factors predict disease outcomes
The medical history information contained in electronic health records (EHR) is a valuable and largely untapped data mining source for predicting patient outcomes and thereby improving treatment. This paper presents a simple but novel evolutionary algorithm (EA) for identifying how various medical history and demographic factors predict clinical outcomes. For this initial study, our EA was tested using synthetic data concerning COVID-19 hospitalization rates and we show that the EA results are more informative than logistic regression, neural network, or decision tree results.
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ARCH-COMP23 Category Report: Hybrid Systems Theorem Proving ARCH-COMP23 Category Report: Continuous and Hybrid Systems with Linear Continuous Dynamics ARCH-COMP23 Category Report: Continuous and Hybrid Systems with Nonlinear Dynamics ARCH-COMP23 Repeatability Evaluation Report ARCH-COMP23 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants
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