Natural Language Processing to Improve Prediction of Incident Atrial Fibrillation Using Electronic Health Records

Figure 1. Overview of the process of extracting and processing codified and narrative electronic health record (EHR) data to determine patient‐level predictors of atrial fibrillation. NLP indicates natural language processing.

Figure 2. Cumulative incidence plots stratified by groups of predicted risk in development cohort. A, Depicts the cumulative risk of atrial fibrillation (AF) by groups defined by <16th percentile (green, <3.6%), 16th to <50th percentile (blue, 3.6%–<6.5%), 50th to <84th percentile (orange, 6.5%–<14.6%), and ≥84th percentile (red, ≥14.6%) of predicted AF risk for the codified+natural language processing (NLP) model. B, Depicts the cumulative risk of AF by groups defined by <16th percentile (green, <3.8%), 16th to <50th percentile (blue, 3.8%–<6.8%), 50th to <84th percentile (orange, 6.8%–<14.4%), and ≥84th percentile (red, ≥14.4%) of predicted AF risk for the codified‐only model.

Figure 3. Calibration plots of observed 5‐year atrial fibrillation (AF) risk vs predicted 5‐year AF risk with patients divided into risk groups based on deciles in the development cohort. A, Depicts the plot of observed 5‐year AF risk (y axis) vs predicted 5‐year AF risk (x axis) for the codified+natural language processing (NLP) model in blue, whereas the optimal calibration is shown in gray. B, Depicts the plot of observed 5‐year AF risk (y axis) vs predicted 5‐year AF risk (x axis) for the codified‐only model in blue, whereas the optimal calibration is shown in gray.

Figure 4. Distributions of predicted risk for codified+natural language processing (NLP), codified‐only, and Cohorts for Heart and Aging Research in Genomic Epidemiology Atrial Fibrillation (CHARGE‐AF) models in the internal validation cohort. AF indicates atrial fibrillation.

Figure 5. Cumulative incidence plots stratified by groups of predicted risk in the internal validation cohort. A, Depicts the cumulative risk of atrial fibrillation (AF) by groups defined by <16th percentile (green, <3.4%), 16th to <50th percentile (blue, 3.4%–<5.8%), 50th to <84th percentile (orange, 5.8%–<12.5%), and ≥84th percentile (red, ≥12.5%) of predicted AF risk for the codified+natural language processing (NLP) model. B, Depicts the cumulative risk of AF by groups defined by <16th percentile (green, <3.7%), 16th to <50th percentile (blue, 3.7%–<6.4%), 50th to <84th percentile (orange, 6.4%–<13.2%), and ≥84th percentile (red, ≥13.2%) of predicted AF risk for the codified‐only model. C, Depicts the cumulative risk of AF by groups defined by <16th percentile (green, <1.8%), 16th to <50th percentile (blue, 1.8%–<3.5%), 50th to <84th percentile (orange, 3.5%–<8.8%), and ≥84th percentile (red, ≥8.8%) of predicted AF risk for the Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE‐AF) model.

Figure 6. Calibration plots of observed 5‐year atrial fibrillation (AF) risk vs predicted 5‐year AF risk with patients divided into risk groups based on deciles in the internal validation cohort. A, Depicts the plot of observed 5‐year AF risk (y axis) vs predicted 5‐year AF risk (x axis) for the codified+natural language processing (NLP) model in blue, whereas the optimal calibration is shown in gray. B, Depicts the plot of observed 5‐year AF risk (y axis) vs predicted 5‐year AF risk (x axis) for the codified‐only model in blue, whereas the optimal calibration is shown in gray. C, Depicts the plot of observed 5‐year AF risk (y axis) vs predicted 5‐year AF risk (x axis) for the Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE‐AF) model in blue, whereas the optimal calibration is shown in gray.