  In this paper, we proposed two different approaches, a rule-based approach
and a machine-learning based approach, to identify active heart failure cases
automatically by analyzing electronic health records (EHR). For the rule-based
approach, we extracted cardiovascular data elements from clinical notes and
matched patients to different colors according their heart failure condition by
using rules provided by experts in heart failure. It achieved 69.4% accuracy
and 0.729 F1-Score. For the machine learning approach, with bigram of clinical
notes as features, we tried four different models while SVM with linear kernel
achieved the best performance with 87.5% accuracy and 0.86 F1-Score. Also, from
the classification comparison between the four different models, we believe
that linear models fit better for this problem. Once we combine the
machine-learning and rule-based algorithms, we will enable hospital-wide
surveillance of active heart failure through increased accuracy and
interpretability of the outputs.
