case_images: PMC7344186_01
case_images: Overview of project design. Our project has four steps. Firstly, we extract medical concepts (i.e., symptoms, lab test, medication, and diagnosis) from free clinical notes of SLE patients' EHR data. Next, we align these medical concepts into structured data format for each note per visit. Then, we sort notes in time series order for each patient. Finally, we comparatively study the performance of three embedding methods: Word2Vec, Med2Vec, and EHR2Vec.
case_images: fgene-11-00630-g0001.jpg
case_images: PMC7344186_01_fgene-11-00630-g0001.jpg
case_images: F1
case_images: The overview of the EHR2Vec workflow is shown in Figure 1. Supplementary Figure 1 illustrated an example of information extraction at different time points and intervals for a patient.
case_images: Deep learning architecture of EHR2Vec. EHR2Vec is developed under deep learning framework including two layers of optimizations. The first layer is based on self-attention structure with multi heads to capture the relationship of different medical concepts within each visit event. The second layer is based on co-occurrence of visits to capture the relationships among visits of patients.
case_images: fgene-11-00630-g0002.jpg
case_images: PMC7344186_01_fgene-11-00630-g0002.jpg
case_images: F2
case_images: As shown in Figure 2, model optimization has two steps.