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Importance: Acute Hepatic Porphyria (AHP) is a group of rare but treatable conditions associated with diagnostic delays of fifteen years on average. The advent of electronic health records (EHR) data and machine learning (ML) may improve the timely recognition of rare diseases like AHP. However, prediction models can be difficult to train given the limited case numbers, unstructured EHR data, and selection biases intrinsic to healthcare delivery., Objective: To train and characterize models for identifying patients with AHP., Design Setting and Participants: This diagnostic study used structured and notes-based EHR data from two centers at the University of California, UCSF (2012-2022) and UCLA (2019-2022). The data were split into two cohorts (referral, diagnosis) and used to develop models that predict: 1) who will be referred for testing of acute porphyria, amongst those who presented with abdominal pain (a cardinal symptom of AHP), and 2) who will test positive, amongst those referred. The referral cohort consisted of 747 patients referred for testing and 99,849 contemporaneous patients who were not. The diagnosis cohort consisted of 72 confirmed AHP cases and 347 patients who tested negative. Cases were female predominant and 6-75 years old at the time of diagnosis. Candidate models used a range of architectures. Feature selection was semi-automated and incorporated publicly available data from knowledge graphs., Main Outcomes and Measures: F-score on an outcome-stratified test set., Results: The best center-specific referral models achieved an F-score of 86-91%. The best diagnosis model achieved an F-score of 92%. To further test our model, we contacted 372 current patients who lack an AHP diagnosis but were predicted by our models as potentially having it (≥ 10% probability of referral, ≥ 50% of testing positive). However, we were only able to recruit 10 of these patients for biochemical testing, all of whom were negative. Nonetheless, post hoc evaluations suggested that these models could identify 71% of cases earlier than their diagnosis date, saving 1.2 years., Conclusions and Relevance: ML can reduce diagnostic delays in AHP and other rare diseases. Robust recruitment strategies and multicenter coordination will be needed to validate these models before they can be deployed., Competing Interests: Potential Competing Interests: VAR receives research support from Alnylam, Takeda, Merck, Genentech, Blueprint Medicines, Stryker, Mitsubishi Tanabe, and Janssen. BW receives research support from Alnylam and Mitsubishi Tanabe, and honoraria for participation in advisory boards from Alnylam, Mitsubishi-Tanabe, and Disc Medicine. KA reports receiving consulting fees, advisory board fees and grants to the university from Alnylam Pharmaceuticals, Recordati Rare Diseases, Mitsubishi Tanabe Pharma America and Disc Medicine. RD, SM, NC, and RD are employees of Alnylam Inc, and JA was an employee of Alnylam Inc at the time of his contribution to this manuscript. All authors declare that no actual competing interests exist.