A new study from University of Michigan researchers found that “combining two health related data sources – genetic information and clinical data – for an individual patient can help predict heart failure.”
The study team aimed to find something “akin to a polygenic risk score, which uses genetic data to assess a patient’s risk of a future disease or condition, using clinical data found in an electronic health record.” They began by training an AI model on “vast amounts of genetic and clinical data,” developing a genome-wide association score for heart failure.
They then pulled clinical data from the “EHRs of de-identified cohorts of Michigan Medicine patients to develop the clinical risk score.” The model is trained to treat the “medical diagnosis codes (known as ICD codes) found in electronic medical records as ‘words’ in human language.” It then analyzes “around 30,000 codes in the data to generate one score associated with the development of heart failure.”
The trained model was able to predict “who would develop heart failure eight years prior to diagnosis. Combining both scores together enabled prediction of heart failure 10 years before diagnosis.” The research team hopes to be able to “improve the model so that it can make predictions based on hypothetical changes in health behaviors.”
