"Figure 2: Visual depiction of the theoretical reasoning behind the clustering algorithm. Figure A depicts the empirical pattern of healthcare visits for related symptoms before the index disease diagnosis (data pictured correspond to tuberculosis). There is an increase in symptomatic healthcare visits before diagnosis. The trend is estimated with two curves: The first segment (flatter) capturesthe period where clinical disease is unlikely to be present, the second segment (steeper) captures symptoms of clinical disease and potential missed opportunities. Figure B depicts how the k-means clustering algorithm is applied to the trends for each potential antecedent condition (note, this is an oversimplified depiction where only the two slope parameters are used to identify k=3 clusters). The central plot depicts examples of clusters of conditions identified based on the slope parameters. The plots on either side of the clustering graph depict examples of trends that might fit the patterns...."
"Figure 4: Examples of trends in top antecedent conditions selected in the “cough” focal cluster prior to tuberculosis. The black dots depict 7-day average counts of visits with the given diagnosis relative to visit frequency. The linear piecewise model used to fit the trend and derive parameter estimates for the cluster analysis is depicted by the red line (see Supplemental Figure 3 for the remaining top 25 conditions)."
"Figure 6: Examples of selected trends in antecedent conditions contained in the “abdominal pain” focal cluster prior to appendicitis. The black dots depict 7-day average counts of visits with the given diagnosis relative to visit frequency. The linear piecewise model used to fit the trend and derive parameter estimates for the cluster analysis is depicted by the red line (see Supplemental Figure 6 for the remaining top 25 conditions)."
"Supplemental Figure 4 – Evaluation of focal cluster based on cough prior to tuberculosis, in terms of number of potential missed opportunities and patients with a diagnostic delay identified. The number of potential missed opportunities and patients that would be identified based on the biologically plausible antecedent conditions identified using the focal cluster are plotted for different values of k. The black line depicts the mean value while the grey shaded region represents values in between the 5 th and 95 th percentile of resulting clusters. The resulting number of potential missed opportunities identified decreases with greater values of k, yet across all cluster considerably more diagnostic delays may be identified compared to using the focal symptom of cough alone."
"Supplemental Figure 7 – Evaluation of focal cluster containing unspecified abdominal pain prior to appendicitis, in terms of number of potential missed opportunities and patients with a diagnostic delay...."
Can #health systems spot missed diagnoses at scale?
Unsupervised clustering found symptoms that spike before a particular #diagnosis to consider clinically-plausible alternative diagnoses, which more than doubled detection of potential diagnostic delays.
doi.org/10.1515/dx-2...
