Machine Learning Based on Natural Language Processing to Detect Cardiac Failure in Clinical Narratives

• URL: http://arxiv.org/abs/2104.03934v1
• Date: Thu, 8 Apr 2021 17:28:43 GMT
• Title: Machine Learning Based on Natural Language Processing to Detect Cardiac Failure in Clinical Narratives
• Authors: Thanh-Dung Le, Rita Noumeir, Jerome Rambaud, Guillaume Sans, and Philippe Jouvet
• Abstract summary: The purpose of the study is to develop a machine learning algorithm that automatically detects whether a patient has a cardiac failure or a healthy condition. A word representation learning technique was employed by using bag-of-word (BoW), term frequency inverse document frequency (TFIDF), and neural word embeddings (word2vec) The proposed framework yielded an overall classification performance with acc, pre, rec, and f1 of 84% and 82%, 85%, and 83%, respectively.
• Score: 0.2936007114555107
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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