BAYESIAN ANALYSIS FOR IMBALANCED POSITIVE-UNLABELLED DIAGNOSIS CODES IN ELECTRONIC HEALTH RECORDS.
BAYESIAN ANALYSIS FOR IMBALANCED POSITIVE-UNLABELLED DIAGNOSIS CODES IN ELECTRONIC HEALTH RECORDS.
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DOI：
10.1214/22-aoas1666
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发表时间：
2023-06
期刊：
影响因子：
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通讯作者：
Liu T
中科院分区：
文献类型：
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作者：
Wang R;Liang Y;Miao Z;Liu T
随着电子健康记录（EHR）的日益普及，健康数据分析师和研究人员在开发预测推理和算法方面取得了重大进展。然而，电子病历数据是臭名昭著的噪音，由于丢失和不准确的输入，尽管信息是丰富的。一个严重的问题是，数据库中只有一小部分患者得到了确诊，而许多其他患者由于没有遵守建议的检查而仍未得到诊断。这一现象导致了所谓的积极-非标签化的情况，标签极不平衡。在本文中，我们提出了一个基于模型的方法来分类的未标记的患者，通过使用贝叶斯有限混合模型。我们还讨论了标签交换的不平衡数据的问题，并提出了一个共识蒙特卡罗方法来解决不平衡的问题，同时提高计算效率。仿真研究表明，我们提出的基于模型的方法优于现有的积极的非标记学习算法。所提出的方法被应用在Cerner EHR上，用于使用实验室测量来检测糖尿病视网膜病变（DR）患者。在EHR数据库中只有3%的确诊率，我们估计实际DR患病率为25%，这与医学文献中报告的结果一致。
With the increasing availability of electronic health records (EHR), significant progress has been made on developing predictive inference and algorithms by health data analysts and researchers. However, the EHR data are notoriously noisy due to missing and inaccurate inputs despite the information is abundant. One serious problem is that only a small portion of patients in the database has confirmatory diagnoses while many other patients remain undiagnosed because they did not comply with the recommended examinations. The phenomenon leads to a so-called positive-unlabelled situation and the labels are extremely imbalanced. In this paper, we propose a model-based approach to classify the unlabelled patients by using a Bayesian finite mixture model. We also discuss the label switching issue for the imbalanced data and propose a consensus Monte Carlo approach to address the imbalance issue and improve computational efficiency simultaneously. Simulation studies show that our proposed model-based approach outperforms existing positive-unlabelled learning algorithms. The proposed method is applied on the Cerner EHR for detecting diabetic retinopathy (DR) patients using laboratory measurements. With only 3% confirmatory diagnoses in the EHR database, we estimate the actual DR prevalence to be 25% which coincides with reported findings in the medical literature.