Table 4.
Patient Data Sample for Deep-KOA Model Evaluationa
ID Patient | Age (Years) | Sex | Total Diagnoses in 3 Years (n) | Total Clinical Visits in 3 Years (n) | Total Medications in 3 Years (n) | Total Days of Medication Prescriptions in 3 Years (Days) | Label | Score |
---|---|---|---|---|---|---|---|---|
A | 58 | Male | 3 | 5 | 7 | 84 | KOA | 0.172 |
B | 63 | Female | 6 | 10 | 6 | 158 | KOA | 0.639 |
C | 59 | Female | 12 | 30 | 24 | 1355 | KOA | 0.942 |
D | 55 | Male | 3 | 3 | 7 | 24 | NonKOA | 0.137 |
E | 37 | Female | 5 | 10 | 8 | 30 | NonKOA | 0.012 |
F | 46 | Female | 11 | 33 | 20 | 307 | NonKOA | 0.124 |
Notes: aTo compare the model performance, three patients of KOA and nonKOA are randomly chosen based on the feature similarity, especially the number of features during three years visiting.
Abbreviations: Deep-KOA, deep learning model for knee osteoarthritis prediction; KOA, knee osteoarthritis.