Weakly Semi-supervised phenotyping using Electronic Health records.
Weakly Semi-supervised phenotyping using Electronic Health records.
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DOI：
10.1016/j.jbi.2022.104175
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发表时间：
2022-10
影响因子：
4.5
通讯作者：
Hong, Chuan
中科院分区：
文献类型：
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作者：
Nogues, Isabelle-Emmanuella;Wen, Jun;Lin, Yucong;Liu, Molei;Tedeschi, Sara K;Geva, Alon;Cai, Tianxi;Hong, Chuan
基于电子健康记录（EHR）的表型分析是生物医学领域中一个关键而又具有挑战性的问题。虽然临床医生通常通过手动图表审查来确定患者级别的诊断，但EHR数据的庞大数量和异质性使得这些任务具有挑战性，耗时且昂贵，从而导致EHR中临床注释的稀缺。弱监督学习算法已成功应用于各种EHR表型问题，因为它们能够利用来自大量未标记样本的信息，以更好地基于更少数量的患者进行预测。然而，大多数弱监督方法都面临着选择正确的截止值来生成最佳分类器的挑战。此外，由于它们只利用信息量最大的特征（即，主要ICD和NLP计数），它们可能无法通过ICD和NLP数据一致地检测到的偶发表型。在本文中，我们提出了一种标签有效的，弱半监督的EHR表型深度学习算法（WSS-DL），它克服了上述限制。WSS-DL通过一系列学习阶段对患者级别的疾病状态进行分类：1）生成银标准标签，2）通过将弱监督深度学习模型拟合到数据来导出增强的银标准标签，其中银标准标签作为结果，高维EHR特征作为输入，以及3）通过将监督学习模型拟合到具有最小数量的金标准标签作为结果的数据来获得最终预测分数和分类器，并将增强银标准标签和最小的信息量最大的EHR特征集作为输入。为了评估WSS-DL在不同表型和医疗机构中的通用性，我们使用来自三个医疗系统的EHR数据，应用WSS-DL对17种疾病进行分类，包括急性和慢性疾病。此外，我们确定了WSSDL所需的最小训练标签数量，以优于现有的监督和半监督表型分析方法。所提出的方法结合了深度学习和弱半监督学习的优势，成功地利用了来自未标记样本的EHR特征中包含的关键表型信息。事实上，深度学习模型处理高维EHR特征的能力使其能够从银标准标签生成强大的表型状态预测。这些预测反过来又在最终的逻辑回归阶段提供了高效的特征，从而在标记数据的特别小的子集（例如n = 40个标记样本）中实现了高表型准确性。我们的方法在标签数量非常少的EHR数据集中的高性能表明其在帮助医生诊断罕见疾病以及容易误诊的疾病方面的潜在价值。
Electronic Health Record (EHR) based phenotyping is a crucial yet challenging problem in the biomedical field. Though clinicians typically determine patient-level diagnoses via manual chart review, the sheer volume and heterogeneity of EHR data renders such tasks challenging, time-consuming, and prohibitively expensive, thus leading to a scarcity of clinical annotations in EHRs. Weakly supervised learning algorithms have been successfully applied to various EHR phenotyping problems, due to their ability to leverage information from large quantities of unlabeled samples to better inform predictions based on a far smaller number of patients. However, most weakly supervised methods are subject to the challenge to choose the right cutoff value to generate an optimal classifier. Furthermore, since they only utilize the most informative features (i.e., main ICD and NLP counts) they may fail for episodic phenotypes that cannot be consistently detected via ICD and NLP data. In this paper, we propose a label-efficient, weakly semi-supervised deep learning algorithm for EHR phenotyping (WSS-DL), which overcomes the limitations above. WSS-DL classifies patient-level disease status through a series of learning stages: 1) generating silver standard labels, 2) deriving enhanced-silver-standard labels by fitting a weakly supervised deep learning model to data with silver standard labels as outcomes and high dimensional EHR features as input, and 3) obtaining the final prediction score and classifier by fitting a supervised learning model to data with a minimal number of gold standard labels as the outcome, and the enhanced-silverstandard labels and a minimal set of most informative EHR features as the input. To assess the generalizability of WSS-DL across different phenotypes and medical institutions, we apply WSS-DL to classify a total of 17 diseases, including both acute and chronic conditions, using EHR data from three healthcare systems. Additionally, we determine the minimum quantity of training labels required by WSSDL to outperform existing supervised and semi-supervised phenotyping methods. The proposed method, in combining the strengths of deep learning and weakly semi-supervised learning, successfully leverages the crucial phenotyping information contained in EHR features from unlabeled samples. Indeed, the deep learning model’s ability to handle high-dimensional EHR features allows it to generate strong phenotype status predictions from silver standard labels. These predictions, in turn, provide highly effective features in the final logistic regression stage, leading to high phenotyping accuracy in notably small subsets of labeled data (e.g. n = 40 labeled samples). Our method’s high performance in EHR datasets with very small numbers of labels indicates its potential value in aiding doctors to diagnose rare diseases as well as conditions susceptible to misdiagnosis.