Figures & data
Table 1 Sample attrition table
Table 2 Demographic characteristics of the evaluated cohorts
Figure 1 The ten most important variables for predicting a diagnosis of fibromyalgia identified from random forest models.
Notes: The level of importance, as shown on the x-axis, ranked for all identified variables based on normalization to 100% for the variable with the largest loss in predicting performance by its omission in the model.
Abbreviation: ER, emergency room.
Abbreviation: ER, emergency room.
Figure 2 Receiver operating characteristic curve modeled using the test dataset.
Notes: Receiver operating characteristic curve of the sensitivity and specificity for predicting the probability of a fibromyalgia diagnosis modeled using the test dataset from the ten most important variables identified from the random forest model. Point A, which denotes a probability value of 0.500, has a sensitivity of 0.641 and a specificity of 0.794. In contrast, point B shows the probability value, 0.446, that provides balance between sensitivity (0.721) and specificity (0.740).
Figure 3 Cumulative distribution functions for the variables identified in the random forest model.
Notes: (A) Number of visits during which diagnostic/laboratory tests were ordered. (B) Number of outpatient visits (excluding office visits). (C) Age. (D) Number of office visits. (E) Number of opioid prescriptions. (F) Number of prescriptions written. (G) Number of pain medication prescriptions (excluding opioids). (H) Number of prescriptions administered (ordered). (I) Number of emergency department visits. (J) Number of musculoskeletal pain conditions.
Table 3 Rules for identifying FM and no-FM subjects based on results of the predictive modeling using a technique known as C5.0 rules
Table S1 Variables put into random forest model