空军军医大学学报2024，Vol.45Issue(9)：983-989,7.DOI:10.13276/j.issn.2097-1656.2024.09.005
基于Cox回归的雷达操纵员认知能力智能化选拔研究
Research on intelligent selection of cognitive ability of radar operators based on Cox regression
摘要
Abstract
Objective To explore the construction of intelligent selection model of radar operators by task evaluation of cognitive ability paradigm based on Cox regression.Methods Univariate Cox regression analysis included 22 radar operation professional recruits and 25 non-professional recruits.Past selection methods,intelligence level,reaction time(RT)and accuracy(ACC)of spatial N-back,three-dimensional 1-back and psychomotor vigilance task(PVT)were selected as independent variables.The dependent variable was the date when the recruits achieved good scores in three consecutive professional examinations for the first time during the training period(120 d).Multivariate Cox regression analysis included 88 radar operation professional recruits and 49 non-professional recruits.Independent variables were determined by univariate Cox regression which was significantly correlated with time variables(P<0.05),and dependent variables were the same as in the univariate Cox regression.Results The final selection model was composed of five factors:past selection methods,3-back ACC,4-back ACC,three-dimensional 1-back RT,and PVT RT(P<0.05).The predicted probability values were presented through a nomogram,and Bootstrap method was used to verify the calibration curves of good rate on the 60th day,the 90th day,and the 120th day,which showed that the predicted probability of the selection model was in good agreement with the actual observation value.The predictive efficiency of the selection model was evaluated by the receiver operating characteristic curve.The results showed that the discrimination ability was better on the 60th day,the 90th day,and the 120th day,where the area under the curve(AUC)on the 60th day was 0.73(P=0.01),and 95%CI was 0.59-0.87;the AUC on the 90th day was 0.86(P=0.01),and 95%CI was 0.80-0.93;and the AUC on the 120th day was 0.76(P=0.01),and 95%CI was 0.68-0.84.For past selection methods,the AUC on the 60th day was 0.55(P=0.44),and 95%CI was 0.42-0.69;the AUC on the 90th day was 0.60(P=0.05),and 95%CI was 0.51-0.69;and the AUC on the 120th day was 0.61(P=0.02),and 95%CI was 0.51-0.70.The statistical difference between the AUC of the selection model and the past selection methods showed that the AUC of the 90th day and the 120th day was significantly different(P<0.05),indicating that the discrimination ability of the selection model is better than that of the past selection methods.Decision curve analysis showed that most of the curve of the selection model was above the curve of the past selection methods,and the threshold width was wider,which indicated that the selection model could get greater net benefits compared with the past selection methods when selecting radar operation professional recruits.Conclusion The selection model constructed in this study is more accurate for the feedback of professional performance and can effectively reduce the selection risk and time cost.关键词
智能化选拔/雷达操纵员/认知能力/Cox回归Key words
intelligent selection/radar operator/cognitive ability/Cox regression分类
医药卫生引用本文复制引用
马晓岩,卢宏亮,张志龙,刘方霆,郭璞,尹海军,朱霞..基于Cox回归的雷达操纵员认知能力智能化选拔研究[J].空军军医大学学报,2024,45(9):983-989,7.基金项目
军委后勤保障部研究项目(22BJZ12,AKJWS221J001) （22BJZ12,AKJWS221J001）
军委后勤保障部综合计划局"十三五"重大项目(AWS17J012) （AWS17J012）
