Open AccessPreventing unnecessary imaging in patients suspect of coronary artery disease through machine learning of electronic health records
Author(s) -
L. Malin Overmars,
Bram van Es,
Floor Groepenhoff,
Mark de Groot,
Gérard Pasterkamp,
Hester M. den Ruijter,
Wouter W. van Solinge,
Imo E. Hoefer,
Saskia Haitjema
Publication year - 2021
Publication title -
european heart journal. digital health
Language(s) - English
Resource type - Journals
ISSN - 2634-3916
DOI - 10.1093/ehjdh/ztab103
Subject(s) - medicine , coronary artery disease , radiology , population , cad , magnetic resonance imaging , cardiology , environmental health , engineering drawing , engineering
With the ageing European population, the incidence of coronary artery disease (CAD) is expected to rise. This will likely result in an increased imaging use. Symptom recognition can be complicated, as symptoms caused by CAD can be atypical, particularly in women. Early CAD exclusion may help to optimize use of diagnostic resources and thus improve the sustainability of the healthcare system. To develop sex-stratified algorithms, trained on routinely available electronic health records (EHRs), raw electrocardiograms, and haematology data to exclude CAD in patients upfront.