Table 2 Summary of the NLP/ML component outcomes.
Phenotype | People Involved | Charts reviewed | Precision | Recall | Comments |
|---|---|---|---|---|---|
Chronic rhinosinusitis | 2 | 126 | 76% → 78–83% | 97% → 100% | Also significant improvement on specificity |
ECG traits | 1–3 | 1050 | Cases: 80–100% Controls: 94–99% | N/A | Unable to extract 1 sub-phenotype; precision varied between sub-phenotypes |
Systemic lupus erythematosus | 2–3 | 1022 | 99% → 96% | 79% → 91% | 2/3 sub-phenotypes performed better at validation site |
Asthma/chronic obstructive pulmonary disease overlap | 1–2 | 300 | 90% → 91% | 38% → 54% | Although overall improved, performed worse at validation site possibly due to how the ML model used counts of features |
Familial hypercholesterolemia | 1–4 | 150 | 96–98% → 74–96% | N/A | Negative predictive value decreased |
Atopic dermatitis | 1–3 | 150 | 73–79% → 72–84% | 51–54% → 63–75% | Mixed results across sub-phenotypes & sites |