Machine learning for an expert system to predict preterm birth risk.
Machine learning for an expert system to predict preterm birth risk.
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DOI：
10.1136/jamia.1994.95153433
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发表时间：
1994-11
期刊：
影响因子：
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通讯作者：
L. Woolery;J. Grzymala-Busse
中科院分区：
文献类型：
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作者：
L. Woolery;J. Grzymala-Busse
目的建立孕妇早产风险评估专家系统原型。正常妊娠期为40周，但由于美国8-12%的新生儿是在妊娠37周之前出生的，因此与早产相关的问题继续困扰着个人、家庭和医疗保健系统。设计一种使用机器学习、统计分析和验证技术的知识库开发方法来分析三个大型数据集（18,890个受试者和214个变量）。研究的依赖（即决定）变量是分娩时的妊娠周数，早产（37周之前）和足月分娩（37周以上）的二分类编码。使用名为“使用粗糙集从示例中学习”（LERS）的程序进行机器学习，产生520条可用规则，并将其输入到原型专家系统中。原型专家系统预测9419例患者早产的准确率为53-88%。结论原型专家系统预测早产的准确率高于传统的人工预测。
OBJECTIVE Develop a prototype expert system for preterm birth risk assessment of pregnant women. Normal gestation involves a term of 40 weeks, but because 8-12% of the newborns in the United States are delivered prior to 37 weeks' gestation, problems associated with prematurity continue to plague individuals, families, and the health care system. DESIGN A knowledge-base development methodology used machine learning, statistical analysis, and validation techniques to analyze three large datasets (18,890 subjects and 214 variables). The dependent (i.e., decision) variable studied was weeks of gestation at delivery, with dichotomous coding of preterm delivery (prior to 37 weeks) and full-term delivery (37+ weeks). RESULTS Machine learning with a program named Learning from Examples using Rough Sets (LERS) induced 520 usable rules that were entered into a prototype expert system. The prototype expert system was 53-88% accurate in predicting preterm delivery for 9,419 patients. CONCLUSION The prototype expert system was more accurate than traditional manual techniques in predicting preterm birth.