Unsupervised machine learning to detect patient subgroups in electronic health records

Unsupervised machine learning to detect patient subgroups in electronic health records

The use of Electronic Health Records (EHR) for reporting patient data has been widely adopted by healthcare providers. This data can encompass many forms of medical information such as disease symptoms, results from laboratory tests, ICD-10 classes and other information from patients. Structured EHR...

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Bibliographic Details
Main Author: Lütz, Elin
Format: Others
Language:English
Published: KTH, Skolan för elektroteknik och datavetenskap (EECS) 2019
Subjects:
Machine learning
unsupervised learning
clustering
EHR
electronic health records
ICD
diagnosis codes.
Maskininlärning
oövervakat lärande
klustring
digitala patientjournaler
diagnoskoder
Computer and Information Sciences
Data- och informationsvetenskap
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-251669

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-251669

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