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

Author:

Hersh William R1ORCID,Cohen Aaron M1ORCID,Nguyen Michelle M1,Bensching Katherine L2,Deloughery Thomas G2

Affiliation:

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

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.

Funder

Alnylam Pharmaceuticals

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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