  The purpose of the study presented herein is to develop a machine learning
algorithm based on natural language processing that automatically detects
whether a patient has a cardiac failure or a healthy condition by using
physician notes in Research Data Warehouse at CHU Sainte Justine Hospital.
First, a word representation learning technique was employed by using
bag-of-word (BoW), term frequency inverse document frequency (TFIDF), and
neural word embeddings (word2vec). Each representation technique aims to retain
the words semantic and syntactic analysis in critical care data. It helps to
enrich the mutual information for the word representation and leads to an
advantage for further appropriate analysis steps. Second, a machine learning
classifier was used to detect the patients condition for either cardiac failure
or stable patient through the created word representation vector space from the
previous step. This machine learning approach is based on a supervised binary
classification algorithm, including logistic regression (LR), Gaussian
Naive-Bayes (GaussianNB), and multilayer perceptron neural network (MLPNN).
Technically, it mainly optimizes the empirical loss during training the
classifiers. As a result, an automatic learning algorithm would be accomplished
to draw a high classification performance, including accuracy (acc), precision
(pre), recall (rec), and F1 score (f1). The results show that the combination
of TFIDF and MLPNN always outperformed other combinations with all overall
performance. In the case without any feature selection, the proposed framework
yielded an overall classification performance with acc, pre, rec, and f1 of 84%
and 82%, 85%, and 83%, respectively. Significantly, if the feature selection
was well applied, the overall performance would finally improve up to 4% for
each evaluation.
