Machine learning based on clinicians’ notes found in electronic health records accurately identifies cases of ANCA-associated vasculitis (AAV), a study demonstrated, AAV is a rare autoimmune disease in which self-reactive antibodies called ANCAs abnormally activate immune neutrophils, causing damage to small blood vessels.
Available rule-based algorithms for AAV case identification rely on EHR information such as ANCA test results and ICD-9 codes. EHR data include free text entered by clinicians to document a patient’s diagnosis, symptoms, and related features.
Researchers then created three datasets to test different machine learning algorithms. After running each dataset through different machine learning models, one model, the hierarchical attention network (HAN) demonstrated the best performance distinguishing AAV from non-AAV cases. Across all three datasets, HAN’s accuracy was greater than 98%. This approach has the potential to identify cases that may be overlooked if only using structured EHR data.
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