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用于开发预测早产专家系统的机器学习

Machine learning for development of an expert system to predict premature birth.

作者信息

Woolery L K, Grzymala-Busse J

机构信息

School of Nursing, University of Missouri, Columbia 65211, USA.

出版信息

Biomed Sci Instrum. 1995;31:29-34.

PMID:7654979
Abstract

Normal pregnancy involves a term of 40 weeks gestation. Problems associated with low birthweight and prematurity continue to plague childbearing families and the healthcare system because 8-12% of all newborns in the United States deliver prior to 37 weeks gestation. The high cost of caring for premature babies increasingly treats all pregnant women as if they are 'high risk' for preterm birth. Artificial intelligence techniques used a machine learning program named LERS1 with large datasets (n = 18,890; 214 variables), statistical analysis, expert verification techniques, and a prototype expert system2 that yielded improved accuracy (53-90%) over existing manual techniques (17-38%) for predicting preterm birth.

摘要

正常妊娠的孕期为40周。与低出生体重和早产相关的问题继续困扰着生育家庭和医疗保健系统,因为在美国,8%至12%的新生儿在妊娠37周前出生。照顾早产儿的高昂成本使得所有孕妇都被视为早产的“高危”人群。人工智能技术使用了一个名为LERS1的机器学习程序,该程序处理了大型数据集(n = 18890;214个变量),运用了统计分析、专家验证技术以及一个原型专家系统2,在预测早产方面,其准确率(53%至90%)高于现有的人工技术(17%至38%)。

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