  Heart failure is a syndrome which occurs when the heart is not able to pump
blood and oxygen to support other organs in the body. Identifying the
underlying themes in the diagnostic codes and procedure reports of patients
admitted for heart failure could reveal the clinical phenotypes associated with
heart failure and to group patients based on their similar characteristics
which could also help in predicting patient outcomes like length of stay. These
clinical phenotypes usually have a probabilistic latent structure and hence, as
there has been no previous work on identifying phenotypes in clinical notes of
heart failure patients using a probabilistic framework and to predict length of
stay of these patients using data-driven artificial intelligence-based methods,
we apply natural language processing technique, topic modeling, to identify the
themes present in diagnostic codes and in procedure reports of 1,200 patients
admitted for heart failure at the University of Illinois Hospital and Health
Sciences System (UI Health). Topic modeling identified twelve themes each in
diagnostic codes and procedure reports which revealed information about
different phenotypes related to various perspectives about heart failure, to
study patients' profiles and to discover new relationships among medical
concepts. Each theme had a set of keywords and each clinical note was labeled
with two themes - one corresponding to its diagnostic code and the other
corresponding to its procedure reports along with their percentage
contribution. We used these themes and their percentage contribution to predict
length of stay. We found that the themes discovered in diagnostic codes and
procedure reports using topic modeling together were able to predict length of
stay of the patients with an accuracy of 61.1% and an Area under the Receiver
Operating Characteristic Curve (ROC AUC) value of 0.828.
