A model-agnostic approach for understanding heart failure risk factors.

ABSTRACT:

Objective

Understanding the risk factors for developing heart failure among patients with type 2 diabetes can contribute to preventing deterioration of quality of life for those persons. Electronic health records (EHR) provide an opportunity to use sophisticated machine learning models to understand and compare the effect of different risk factors for developing HF. As the complexity of the model increases, however, the transparency of the model often decreases. To interpret the results, we aimed to develop a model-agnostic approach to shed light on complex models and interpret the effect of features on developing heart failure. Using the HealthFacts EHR database of the Cerner EHR, we extracted the records of 723 patients with at least 6 yeas of follow up of type 2 diabetes, of who

SUBMITTER: Miran SM 

PROVIDER: S-EPMC8130447 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature