Computer Science Department, Georgia State University, Atlanta, GA 30303, USA.
Advance Analytics, IQVIA Inc., Plymouth Meeting, PA 19462, USA.
J Biomed Inform. 2021 Aug;120:103840. doi: 10.1016/j.jbi.2021.103840. Epub 2021 Jun 15.
Electronic health records contain patient's information that can be used for health analytics tasks such as disease detection, disease progression prediction, patient profiling, etc. Traditional machine learning or deep learning methods treat EHR entities as individual features, and no relationships between them are taken into consideration. We propose to evaluate the relationships between EHR features and map them into Procedures, Prescriptions, and Diagnoses (PPD) tensor data, which can be formatted as images. The mapped images are then fed into deep convolutional networks for local pattern and feature learning. We add this relationship-learning part as a boosting module on a commonly used classical machine learning model. Experiments were performed on a Chronic Lymphocytic Leukemia dataset for treatment initiation prediction. Experimental results show that the proposed approach has better real world modeling performance than the baseline models in terms of prediction precision.
电子健康记录包含可用于健康分析任务（如疾病检测、疾病进展预测、患者分析等）的患者信息。传统的机器学习或深度学习方法将 EHR 实体视为单独的特征，而不考虑它们之间的关系。我们建议评估 EHR 特征之间的关系，并将它们映射到 Procedures、Prescriptions 和 Diagnoses (PPD) 张量数据中，这些数据可以格式化为图像。然后将映射的图像输入到深度卷积网络中进行局部模式和特征学习。我们将此关系学习部分作为一个提升模块添加到常用的经典机器学习模型中。在慢性淋巴细胞白血病数据集上进行了治疗起始预测的实验。实验结果表明，与基线模型相比，所提出的方法在预测精度方面具有更好的实际建模性能。
