Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria

Author:

Cohen Aaron M.ORCID,Chamberlin Steven,Deloughery Thomas,Nguyen Michelle,Bedrick Steven,Meninger Stephen,Ko John J.ORCID,Amin Jigar J.,Wei Alex J.,Hersh William

Funder

Alnylam Pharmaceuticals

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Reference25 articles.

1. Clinical data reuse or secondary use: current status and potential future progress;SM Meystre;Yearbook of medical informatics,2017

2. How many rare diseases are there?;M Haendel;Nat Rev Drug Discov,2020

3. General knowledge and opinion of future health care and non-health care professionals on rare diseases;E Ramalle-Gómara;J Eval Clin Pract,2015

4. Garg R, Dong S, Shah S, Jonnalagadda SR. A bootstrap machine learning approach to identify rare disease patients from electronic health records. arXiv preprint arXiv:160901586. 2016.

5. Colbaugh R, Glass K, Rudolf C, Global MTV. Learning to Identify Rare Disease Patients from Electronic Health Records. In: AMIA Annual Symposium Proceedings. American Medical Informatics Association; 2018. p. 340.

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