Reducing diagnostic delays in Acute Hepatic Porphyria using electronic health records data and machine learning: a multicenter development and validation study.

Can machine learning help identify undiagnosed patients with Acute Hepatic Porphyria (AHP), a group of rare diseases? Using electronic health records (EHR) data from two centers we developed models to predict: 1) who will be referred for AHP testing, and 2) who will test positive. The best models achieved 89-93% accuracy on the test set. These models appeared capable of recognizing 71% of the cases earlier than their true diagnosis date, reducing diagnostic delays by an average of 1.2 years. Machine learning models trained using EHR data can help reduce diagnostic delays in rare diseases like AHP.