Nashville, Tenn.-based Vanderbilt University Medical Center researchers applied artificial intelligence to de-identified patient EHR data to identify sub-phenotypes of cardiovascular disease.
Researchers published the study in the Journal of Biomedical Informatics. For the research, the VUMC team gathered 12,380 de-identified patient records of individuals who had been diagnosed with CVD. Records extended at least 10 years prior to the patient's CVD diagnosis.
After applying an automated scan of the data, researchers discovered 1,068 distinct patient phenotypes in the dataset. The team then used machine learning in conjunction with a technique called tensor decomposition to identify the long-term emergence of 14 distinct CVD patient subtypes. The researchers then compared the subsequent myocardial infarction, or heart attack, rates among the six most prevalent sub-phenotypes.
Results of the study showed that heart attack risk was noticeably different among the six most prevalent subtypes. Through an association analysis with estimated CVD risk for each subtype, researchers found that some phenotypic topics such as Vitamin D deficiency and depression could not be explained by conventional risk factors.
Researchers concluded that because the six most prevalent sub-phenotypes presented noticeably different risks of subsequent myocardial infarction, the topics may identify clinically relevant sub-phenotypes of CVD.