Clinical study applying machine learning to detect a rare disease: results and lessons learned

William R Hersh; Aaron M Cohen; Michelle M Nguyen; Katherine L Bensching; Thomas G Deloughery

doi:10.1093/jamiaopen/ooac053

Clinical study applying machine learning to detect a rare disease: results and lessons learned

JAMIA Open. 2022 Jun 30;5(2):ooac053. doi: 10.1093/jamiaopen/ooac053. eCollection 2022 Jul.

Authors

William R Hersh¹, Aaron M Cohen¹, Michelle M Nguyen¹, Katherine L Bensching², Thomas G Deloughery²

Affiliations

¹ Department of Medical Informatics & Clinical Epidemiology, School of Medicine, Oregon Health & Science University, Portland, Oregon, USA.
² Department of Medicine, School of Medicine, Oregon Health & Science University, Portland, Oregon, USA.

Abstract

Machine learning has the potential to improve identification of patients for appropriate diagnostic testing and treatment, including those who have rare diseases for which effective treatments are available, such as acute hepatic porphyria (AHP). We trained a machine learning model on 205 571 complete electronic health records from a single medical center based on 30 known cases to identify 22 patients with classic symptoms of AHP that had neither been diagnosed nor tested for AHP. We offered urine porphobilinogen testing to these patients via their clinicians. Of the 7 who agreed to testing, none were positive for AHP. We explore the reasons for this and provide lessons learned for further work evaluating machine learning to detect AHP and other rare diseases.

Keywords: acute intermittent; clinical study; electronic health records; machine learning; porphyria.