ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
Extracting Smoking Status from Electronic Health Records Using NLP and Deep Learning.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Extracting social determinants of health from electronic health records using natural language processing: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- RELEAP: reinforcement-enhanced label-efficient active phenotyping for electronic health records.JAMIA open · 2026Article
- Natural Language Processing for Substance Use Disorder Information Extraction: A Systematic Literature Review.Current addiction reports · 2026Review
- The Impact of Collaborative Documentation on Person-Centered Care: Textual Analysis of Clinical Notes.JMIR medical informatics · 2024Article
- Social Phenotyping for Cardiovascular Risk Stratification in Electronic Health Registries.Current atherosclerosis reports · 2024Review
- Identification of patients' smoking status using an explainable AI approach: a Danish electronic health records case study.BMC medical research methodology · 2024Article
- Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias.Journal of biomedical informatics · 2024Article
- Big Data, Big Insights: Leveraging Data Analytics to Unravel Cardiovascular Exposome Complexities.Methodist DeBakey cardiovascular journal · 2024Review
- Benchmarking Transformer-Based Models for Identifying Social Determinants of Health in Clinical Notes.Proceedings. IEEE International Conference on Healthcare Informatics · 2023Article
- Automating Access to Real-World Evidence.JTO clinical and research reports · 2022Article
- Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice.Radiology. Artificial intelligence · 2022Review
- Promoting health equity for deaf patients through the electronic health record.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Balancing Model Complexity and Clinical Deployability in Deep Learning for Sociodemographic Information Extraction.Journal of primary care & community healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
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.
Identifiers
32477672PMC7233082What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.