  Biomedical research is increasingly employing real world evidence (RWE) to
foster discoveries of novel clinical phenotypes and to better characterize long
term effect of medical treatments. However, due to limitations inherent in the
collection process, RWE often lacks key features of patients, particularly when
these features cannot be directly encoded using data standards such as ICD-10.
Here we propose a novel data-driven statistical machine learning approach,
named Feature Imputation via Local Likelihood (FILL), designed to infer missing
features by exploiting feature similarity between patients. We test our method
using a particularly challenging problem: differentiating heart failure
patients with reduced versus preserved ejection fraction (HFrEF and HFpEF
respectively). The complexity of the task stems from three aspects: the two
share many common characteristics and treatments, only part of the relevant
diagnoses may have been recorded, and the information on ejection fraction is
often missing from RWE datasets. Despite these difficulties, our method is
shown to be capable of inferring heart failure patients with HFpEF with a
precision above 80% when considering multiple scenarios across two RWE datasets
containing 11,950 and 10,051 heart failure patients. This is an improvement
when compared to classical approaches such as logistic regression and random
forest which were only able to achieve a precision < 73%. Finally, this
approach allows us to analyse which features are commonly associated with HFpEF
patients. For example, we found that specific diagnostic codes for atrial
fibrillation and personal history of long-term use of anticoagulants are often
key in identifying HFpEF patients.
