Work

Extracting Smoking Status from Electronic Health Records Using NLP and Deep Learning.

Year: 2020

Type: article

Abstract: Half a million people die every year from smoking-related issues across the United States. It is essential to identify individuals who are tobacco-dependent in order to implement preventive measures. In this study, we investigate the effectiveness of deep learning models to extract smoking status of patients from clinical progress notes. A Natural Language Processing (NLP) Pipeline was built that cleans the progress notes prior to processing by three deep neural networks: a CNN, a unidirectional LSTM, and a bidirectional LSTM. Each of these models was trained with a pre- trained or a post-trained word embedding layer. Three traditional machine learning models were also employed to compare against the neural networks. Each model has generated both binary and multi-class label classification. Our results showed that the CNN model with a pre-trained embedding layer performed the best for both binary and multi- class label classification.

Source: PubMed Central

Authors: Suraj Rajendran, Umit Topaloglu

Institutions: Georgia Institute of Technology, Wake Forest University Health Sciences, Wake Forest University

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Cancer Institute

Awards: Wake Forest Clinical and Translational Science Award, Cancer Center Support Grant