...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>JMIR public health and surveillance. >Using Predictive Analytics to Identify Children at High Risk of Defaulting From a Routine Immunization Program: Feasibility Study
【24h】

Using Predictive Analytics to Identify Children at High Risk of Defaulting From a Routine Immunization Program: Feasibility Study

机译：使用预测分析从常规免疫计划中识别出存在严重违约风险的儿童：可行性研究

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Background: Despite the availability of free routine immunizations in low- and middle-income countries, many children are not completely vaccinated, vaccinated late for age, or drop out from the course of the immunization schedule. Without the technology to model and visualize risk of large datasets, vaccinators and policy makers are unable to identify target groups and individuals at high risk of dropping out; thus default rates remain high, preventing universal immunization coverage. Predictive analytics algorithm leverages artificial intelligence and uses statistical modeling, machine learning, and multidimensional data mining to accurately identify children who are most likely to delay or miss their follow-up immunization visits. Objective: This study aimed to conduct feasibility testing and validation of a predictive analytics algorithm to identify the children who are likely to default on subsequent immunization visits for any vaccine included in the routine immunization schedule. Methods: The algorithm was developed using 47,554 longitudinal immunization records, which were classified into the training and validation cohorts. Four machine learning models (random forest; recursive partitioning; support vector machines, SVMs; and C-forest) were used to generate the algorithm that predicts the likelihood of each child defaulting from the follow-up immunization visit. The following variables were used in the models as predictors of defaulting: gender of the child, language spoken at the child’s house, place of residence of the child (town or city), enrollment vaccine, timeliness of vaccination, enrolling staff (vaccinator or others), date of birth (accurate or estimated), and age group of the child. The models were encapsulated in the predictive engine, which identified the most appropriate method to use in a given case. Each of the models was assessed in terms of accuracy, precision (positive predictive value), sensitivity, specificity and negative predictive value, and area under the curve (AUC). Results: Out of 11,889 cases in the validation dataset, the random forest model correctly predicted 8994 cases, yielding 94.9% sensitivity and 54.9% specificity. The C-forest model, SVMs, and recursive partitioning models improved prediction by achieving 352, 376, and 389 correctly predicted cases, respectively, above the predictions made by the random forest model. All models had a C-statistic of 0.750 or above, whereas the highest statistic (AUC 0.791, 95% CI 0.784-0.798) was observed in the recursive partitioning algorithm. Conclusions: This feasibility study demonstrates that predictive analytics can accurately identify children who are at a higher risk for defaulting on follow-up immunization visits. Correct identification of potential defaulters opens a window for evidence-based targeted interventions in resource limited settings to achieve optimal immunization coverage and timeliness.
机译：背景：尽管在低收入和中等收入国家中可以进行免费的常规免疫接种，但许多儿童仍未完全接种疫苗，年龄较晚接种疫苗或未从免疫接种计划中辍学。如果没有用于大型数据集风险建模和可视化的技术，疫苗接种者和政策制定者将无法确定高辍学风险的目标人群和个人。因此，违约率仍然很高，阻止了普遍免疫接种。预测分析算法利用人工智能，并使用统计建模，机器学习和多维数据挖掘来准确识别最有可能延迟或错过后续免疫访问的儿童。目的：本研究旨在进行可行性分析和预测分析算法的验证，以识别出那些可能在常规免疫日程中接种的任何疫苗在随后的免疫访问中不存在的儿童。方法：该算法是根据47554条纵向免疫记录开发的，这些记录分为训练和验证队列。四种机器学习模型（随机森林，递归分区，支持向量机，SVM和C-forest）用于生成算法，该算法可预测每个儿童在后续免疫访问中违约的可能性。在模型中，以下变量用作默认值的预测因子：孩子的性别，孩子在家里说的语言，孩子的住所（城镇或城市），登记疫苗，疫苗接种的及时性，登记人员（疫苗接种者或其他） ），出生日期（准确或估计的）以及孩子的年龄段。这些模型被封装在预测引擎中，该引擎确定了在给定情况下最适合使用的方法。对每个模型进行了准确性，精密度（阳性预测值），敏感性，特异性和阴性预测值以及曲线下面积（AUC）方面的评估。结果：在验证数据集中的11889例病例中，随机森林模型正确预测了8994例病例，灵敏度为94.9％，特异性为54.9％。 C森林模型，SVM和递归分区模型通过分别比随机森林模型做出的预测高352、376和389个正确预测的情况，改善了预测。所有模型的C统计量均为0.750或更高，而在递归分区算法中观察到最高的统计量（AUC 0.791，95％CI 0.784-0.798）。结论：这项可行性研究表明，预测分析可以准确地识别出在后续免疫访问中违约风险较高的儿童。正确识别潜在的违法者会在资源有限的环境中为基于证据的有针对性的干预措施打开一个窗口，以实现最佳的免疫覆盖率和及时性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号