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首页> 外文期刊>BMC Psychiatry >Improving risk prediction accuracy for new soldiers in the U.S. Army by adding self-report survey data to administrative data
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Improving risk prediction accuracy for new soldiers in the U.S. Army by adding self-report survey data to administrative data

机译：通过将自我报告调查数据添加到管理数据中来提高美国陆军新兵的风险预测准确性

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High rates of mental disorders, suicidality, and interpersonal violence early in the military career have raised interest in implementing preventive interventions with high-risk new enlistees. The Army Study to Assess Risk and Resilience in Servicemembers (STARRS) developed risk-targeting systems for these outcomes based on machine learning methods using administrative data predictors. However, administrative data omit many risk factors, raising the question whether risk targeting could be improved by adding self-report survey data to prediction models. If so, the Army may gain from routinely administering surveys that assess additional risk factors. The STARRS New Soldier Survey was administered to 21,790 Regular Army soldiers who agreed to have survey data linked to administrative records. As reported previously, machine learning models using administrative data as predictors found that small proportions of high-risk soldiers accounted for high proportions of negative outcomes. Other machine learning models using self-report survey data as predictors were developed previously for three of these outcomes: major physical violence and sexual violence perpetration among men and sexual violence victimization among women. Here we examined the extent to which this survey information increases prediction accuracy, over models based solely on administrative data, for those three outcomes. We used discrete-time survival analysis to estimate a series of models predicting first occurrence, assessing how model fit improved and concentration of risk increased when adding the predicted risk score based on survey data to the predicted risk score based on administrative data. The addition of survey data improved prediction significantly for all outcomes. In the most extreme case, the percentage of reported sexual violence victimization among the 5% of female soldiers with highest predicted risk increased from 17.5% using only administrative predictors to 29.4% adding survey predictors, a 67.9% proportional increase in prediction accuracy. Other proportional increases in concentration of risk ranged from 4.8% to 49.5% (median?=?26.0%). Data from an ongoing New Soldier Survey could substantially improve accuracy of risk models compared to models based exclusively on administrative predictors. Depending upon the characteristics of interventions used, the increase in targeting accuracy from survey data might offset survey administration costs.
机译：在军事生涯的早期，精神疾病，自杀倾向和人际暴力的高发生率引起了人们对高风险新兵实施预防性干预的兴趣。陆军评估服役人员风险和应变能力的研究（STARRS）基于使用行政数据预测器的机器学习方法，针对这些结果开发了风险目标系统。但是，行政数据忽略了许多风险因素，这引发了一个问题，即是否可以通过将自我报告调查数据添加到预测模型中来改善风险目标。如果这样，陆军可能会从例行的评估其他风险因素的调查中受益。 STARRS新兵调查是对21,790名常规陆军士兵进行的，他们同意将调查数据与行政记录相关联。如前所述，使用行政数据作为预测指标的机器学习模型发现，高风险士兵的一小部分造成了负面结果。以前针对这些结果中的三个结果，开发了使用自我报告调查数据作为预测变量的其他机器学习模型：男性中的主要身体暴力和性暴力，女性中的性暴力受害。在这里，我们检查了针对这三个结果的调查信息在多大程度上基于仅基于管理数据的模型中提高了预测准确性的程度。我们使用离散时间生存分析来估计一系列预测首次发生的模型，评估在将基于调查数据的预测风险评分与基于管理数据的预测风险评分相加时，模型的拟合度如何提高以及风险集中度增加。添加调查数据可以显着改善所有结果的预测。在最极端的情况下，有最高预测风险的5％女兵中报告的性暴力受害百分比从仅使用行政预测变量的17.5％增加到添加调查预测变量的29.4％，预测准确性成比例增加67.9％。风险集中度的其他成比例增长范围为4.8％至49.5％（中位数= 26.0％）。与仅基于行政预测因素的模型相比，正在进行的新士兵调查的数据可以大大提高风险模型的准确性。根据所使用的干预措施的特征，调查数据确定目标准确性的提高可能会抵消调查管理成本。

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