Acute hepatic porphyria (AHP) is a rare but treatable condition with an average diagnostic delay of 15 years. Utilizing electronic health records (EHR) data and machine learning (ML) can potentially improve the timely recognition of AHP. This study used structured and notes-based EHR data from UCSF and UCLA to develop models predicting who will be referred for AHP testing and who will test positive. The referral model achieved an F-score of 86%-91%, and the diagnosis model achieved an F-score of 92%.
Although recruitment for testing among undiagnosed predicted patients was low, post hoc evaluations indicated that the models could identify 71% of cases earlier, potentially saving 1.2 years in diagnostic time. Future work requires robust recruitment strategies and multi-center coordination to validate these models before clinical deployment.
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