Machine Learning of Patient Characteristics to Predict Admission Outcomes in the Undiagnosed Diseases Network-Reference-Cited by-同舟云学术

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Machine Learning of Patient Characteristics to Predict Admission Outcomes in the Undiagnosed Diseases Network

Published:2021-02-25 Issue:2 Volume:4 Page:e2036220
ISSN:2574-3805
Container-title:JAMA Network Open
language:en
Short-container-title:JAMA Netw Open

Author:

Amiri Hadi¹²,Kohane Isaac S.¹,

Affiliation:

1. Department of Biomedical Informatics, Harvard University, Boston, Massachusetts

2. Department of Computer Science, University of Massachusetts, Lowell

Publisher

American Medical Association (AMA)

Subject

General Medicine

Link

https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2776897/amiri_2021_oi_201081_1613710249.16048.pdf

Reference26 articles.

1. Classification and codification of rare diseases.;Kodra;J Clin Epidemiol,2012

2. The genetic basis of mendelian phenotypes: discoveries, challenges, and opportunities.;Chong;Am J Hum Genet,2015

3. Effect of genetic diagnosis on patients with previously undiagnosed disease.;Splinter;N Engl J Med,2018

4. Why rare diseases are an important medical and social issue.;Schieppati;Lancet,2008

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. The Undiagnosed Diseases Network: Characteristics of solvable applicants and diagnostic suggestions for nonaccepted ones;Genetics in Medicine;2024-10

2. Integration of artificial intelligence and multi-omics in kidney diseases;Fundamental Research;2023-01

3. Diagnostic journeys: characterization of patients and diagnostic outcomes from an academic second opinion clinic;Diagnosis;2022-05-23

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