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In this paper, we proposed two different approaches, a rule-based approachand a machine-learning based approach, to identify active heart failure casesautomatically by analyzing electronic health records (EHR). For the rule-basedapproach, we extracted cardiovascular data elements from clinical notes andmatched patients to different colors according their heart failure condition byusing rules provided by experts in heart failure. It achieved 69.4% accuracyand 0.729 F1-Score. For the machine learning approach, with bigram of clinicalnotes as features, we tried four different models while SVM with linear kernelachieved the best performance with 87.5% accuracy and 0.86 F1-Score. Also, fromthe classification comparison between the four different models, we believethat linear models fit better for this problem. Once we combine themachine-learning and rule-based algorithms, we will enable hospital-widesurveillance of active heart failure through increased accuracy andinterpretability of the outputs.

Using Natural Language Processing to Screen Patients with Active Heart Failure: An Exploration for Hospital-wide Surveillance