Machine learning mines EHRs to predict heart failure

The widespread implementation of electronic health records (EHRs) has proved to be a bumpy ride for many. But the sheer amount of data available in digital form carries with it plenty of potential. Recent work by scientists from IBM and Sutter Health developed artificial intelligence that can uncover pre-diagnostic heart failure through EHRs. A study, published in Circulation: Cardiovascular Quality and Outcomes, included a model that used 1,684 heart failure cases along with 13,525 sex, age-category and clinic matched controls for modeling purposes. "Model performance was most strongly influenced by the diversity of data, basic feature construction and the length of the observation window," wrote Kenny Ng, research staff member in the Center for Computational Health and first author of the study. "In raw form, EHR data are highly diverse, represented by thousands of variants for disease coding, medication orders, laboratory measures, and other data types.