面向胎盘植入产前诊断的医学语义特征提取算法

Algorithm for Feature Extraction with Effective Medical Meaning for the Prenatal Diagnosis of Placenta Accreta

胎盘植入由于其临床特征隐匿,尚无一种敏感性、特异性高的产前诊断手段,因此文中将数据的特征提取方法引入胎盘植入产前诊断领域,从特征相关性的角度,提出胎盘植入有效医学语义的多目标特征优化问题,并给出求解该问题的一种改进的非支配排序遗传算法II(NSGA-II).基于实际胎盘植入相关临床数据的计算结果表明,文中算法能从复杂的胎盘植入相关临床数据中提取具有胎盘植入有效语义的特征集合.经过接收者操作特征(ROC)曲线分析,提取的特征医学语义具有较高的诊断价值,可为产科医师研究胎盘植入的发病机制和及时产前诊断提供有效的辅助手段.文中研究还发现,一些临床生化检查指标具有重要作用,可作为胎盘植入产前诊断的有效依据.

Due to inconspicuous clinical characteristics of placenta accreta, there is no prenatal diagnosis methods with high sensitivity and specificity in clinical medicine. In this paper, feature selection method is introduced into the prenatal diagnosis of placenta accreta. From the view of feature correlation, a multi-objective feature optimization problem is formulated to extract features with effective medical meaning for the prenatal diagnosis of placenta accreta, and then an improved non-dominated sorting genetic algorithm II （ NSGA-II） is described to solve this problem. The computational result based on real clinical data for placenta accreta shows that the proposed method can extract placenta accreta features with effective medical meaning from complex clinical data of placenta accreta. The analysis based on receiver operating＆amp;nbsp;characteristic （ ROC ） curve shows that medical meaning of the extracted features has high diagnostic values, and it can be an effective decision tool for obstetricians to study the pathogenesis of placenta accreta and to make a timely prenatal diagnosis. The study reveals that some biochemistry characteristics in real diagnosis are very important and it can provide a reliable criterion for the prenatal diagnosis of placenta accreta.