PDF(Pubmed)
Abstract：
Menopause is a normal transition in a woman\'s life. For some women, it is a stage without significant difficulties; for others, menopause symptoms can severely affect their quality of life. This study developed and validated a case definition for problematic menopause using Canadian primary care electronic medical records, which is an essential step in examining the condition and improving quality of care.
We used data from the Canadian Primary Care Sentinel Surveillance Network including billing and diagnostic codes, diagnostic free-text, problem list entries, medications, and referrals. These data formed the basis of an expert-reviewed reference standard data set and contained the features that were used to train a machine learning model based on classification and regression trees. An ad hoc feature importance measure coupled with recursive feature elimination and clustering were applied to reduce our initial 86,000 element feature set to a few tens of the most relevant features in the data, while class balancing was accomplished with random under- and over-sampling. The final case definition was generated from the tree-based machine learning model output combined with a feature importance algorithm. Two independent samples were used: one for training / testing the machine learning algorithm and the other for case definition validation.
We randomly selected 2,776 women aged 45-60 for this analysis and created a case definition, consisting of two occurrences within 24 months of International Classification of Diseases, Ninth Revision, Clinical Modification code 627 (or any sub-codes) OR one occurrence of Anatomical Therapeutic Chemical classification code G03CA (or any sub-codes) within the patient chart, that was highly effective at detecting problematic menopause cases. This definition produced a sensitivity of 81.5% (95% CI: 76.3-85.9%), specificity of 93.5% (91.9-94.8%), positive predictive value of 73.8% (68.3-78.6%), and negative predictive value of 95.7% (94.4-96.8%).
Our case definition for problematic menopause demonstrated high validity metrics and so is expected to be useful for epidemiological study and surveillance. This case definition will enable future studies exploring the management of menopause in primary care settings.
We used data from the Canadian Primary Care Sentinel Surveillance Network including billing and diagnostic codes, diagnostic free-text, problem list entries, medications, and referrals. These data formed the basis of an expert-reviewed reference standard data set and contained the features that were used to train a machine learning model based on classification and regression trees. An ad hoc feature importance measure coupled with recursive feature elimination and clustering were applied to reduce our initial 86,000 element feature set to a few tens of the most relevant features in the data, while class balancing was accomplished with random under- and over-sampling. The final case definition was generated from the tree-based machine learning model output combined with a feature importance algorithm. Two independent samples were used: one for training / testing the machine learning algorithm and the other for case definition validation.
We randomly selected 2,776 women aged 45-60 for this analysis and created a case definition, consisting of two occurrences within 24 months of International Classification of Diseases, Ninth Revision, Clinical Modification code 627 (or any sub-codes) OR one occurrence of Anatomical Therapeutic Chemical classification code G03CA (or any sub-codes) within the patient chart, that was highly effective at detecting problematic menopause cases. This definition produced a sensitivity of 81.5% (95% CI: 76.3-85.9%), specificity of 93.5% (91.9-94.8%), positive predictive value of 73.8% (68.3-78.6%), and negative predictive value of 95.7% (94.4-96.8%).
Our case definition for problematic menopause demonstrated high validity metrics and so is expected to be useful for epidemiological study and surveillance. This case definition will enable future studies exploring the management of menopause in primary care settings.
摘要：
背景：更年期是女性生活中的正常过渡。对一些女人来说,这是一个没有重大困难的阶段；对其他人来说，更年期症状会严重影响他们的生活质量。这项研究使用加拿大初级保健电子病历开发并验证了有问题的更年期的病例定义，这是检查病情和提高护理质量的重要步骤。
方法：我们使用了来自加拿大初级保健前哨监控网络的数据，包括账单和诊断代码，诊断自由文本，问题列表条目，药物,和转介。这些数据构成了专家审查的参考标准数据集的基础，并包含用于基于分类和回归树训练机器学习模型的特征。应用了一种临时特征重要性度量，再加上递归特征消除和聚类，将我们最初的86,000个元素特征集减少到数据中几十个最相关的特征，而班级平衡是通过随机欠抽样和过抽样来实现的。最终的案例定义是从基于树的机器学习模型输出与特征重要性算法结合生成的。使用两个独立的样本:一个用于训练/测试机器学习算法,另一个用于案例定义验证。
结果：我们随机选择了2,776名45-60岁的女性进行此分析，并创建了案例定义，由国际疾病分类24个月内的两次事件组成，第九次修订,临床修改代码627（或任何子代码）或在患者图表中出现一次解剖治疗化学分类代码G03CA（或任何子代码），这在检测有问题的更年期病例方面非常有效。该定义产生的灵敏度为81.5%（95%CI：76.3-85.9%），特异性为93.5%（91.9-94.8%），阳性预测值为73.8%（68.3-78.6%），阴性预测值为95.7%(94.4-96.8%)。
结论：我们对有问题的更年期的案例定义证明了高有效性指标，因此有望用于流行病学研究和监测。此病例定义将使未来的研究探索初级保健机构中更年期的管理。
方法：我们使用了来自加拿大初级保健前哨监控网络的数据，包括账单和诊断代码，诊断自由文本，问题列表条目，药物,和转介。这些数据构成了专家审查的参考标准数据集的基础，并包含用于基于分类和回归树训练机器学习模型的特征。应用了一种临时特征重要性度量，再加上递归特征消除和聚类，将我们最初的86,000个元素特征集减少到数据中几十个最相关的特征，而班级平衡是通过随机欠抽样和过抽样来实现的。最终的案例定义是从基于树的机器学习模型输出与特征重要性算法结合生成的。使用两个独立的样本:一个用于训练/测试机器学习算法,另一个用于案例定义验证。
结果：我们随机选择了2,776名45-60岁的女性进行此分析，并创建了案例定义，由国际疾病分类24个月内的两次事件组成，第九次修订,临床修改代码627（或任何子代码）或在患者图表中出现一次解剖治疗化学分类代码G03CA（或任何子代码），这在检测有问题的更年期病例方面非常有效。该定义产生的灵敏度为81.5%（95%CI：76.3-85.9%），特异性为93.5%（91.9-94.8%），阳性预测值为73.8%（68.3-78.6%），阴性预测值为95.7%(94.4-96.8%)。
结论：我们对有问题的更年期的案例定义证明了高有效性指标，因此有望用于流行病学研究和监测。此病例定义将使未来的研究探索初级保健机构中更年期的管理。
