ArticleInternational journal of medical informatics2023
Leveraging natural language processing to identify eligible lung cancer screening patients with the electronic health record.
Article in International journal of medical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Integrating Deep Learning of Low-Dose CT Imaging With Clinical Data for Lung Cancer Risk Prediction.Chest · 2026Article
- Review
- Using large language models to enhance clinically-driven missing data recovery algorithms in electronic health records.JAMIA open · 2026Article
- Review
- Fine-tuned lightweight language models for structured extraction of liver cancer imaging free-text report: a comparative analysis with existing large language models.BMC medical imaging · 2026Article
- Artificial intelligence in preventive care in primary health care settings: a scoping review.Archives of medical sciences. Atherosclerotic diseases · 2026Article
- Implementation of a lung cancer screening program at an urban safety-net hospital: enabling solutions via stakeholder engagement.Frontiers in public health · 2026Article
- Integrating rule-based NLP and large language models for statin information extraction from clinical notes.International journal of medical informatics · 2026Article
- SmokeBERT: A Bidirectional Encoder Representations From Transformers-Based Model for Quantitative Smoking History Extraction From Clinical Narratives to Improve Lung Cancer Screening.JCO clinical cancer informatics · 2025Article
- Leveraging large language models to extract smoking history from clinical notes for lung cancer surveillance.NPJ digital medicine · 2025Article
- Natural language processing improves reliable identification of COVID-19 compared to diagnostic codes alone.American journal of epidemiology · 2025Article
- Artificial Intelligence-Based Methods: The Path Forward in Achieving Equity in Lung Cancer Screening and Evaluation.Cancer innovation · 2025Article
- Decoding Recurrence in Early-Stage and Locoregionally Advanced Non-Small Cell Lung Cancer: Insights From Electronic Health Records and Natural Language Processing.JCO clinical cancer informatics · 2025Article
- Toward a Computable Phenotype for Determining Eligibility of Lung Cancer Screening Using Electronic Health Records.JCO clinical cancer informatics · 2025Article
- Optimal Pathways to Lung Cancer Screening in Primary Care Settings: A Scoping Review.Current oncology (Toronto, Ont.) · 2024Article
- Article
- Lung Cancer Screening Before and After a Multifaceted Electronic Health Record Intervention: A Nonrandomized Controlled Trial.JAMA network open · 2024Article
- Characteristics of lung cancer screening eligible population in the US and prediction of the eligibility with simplified criteria.Translational cancer research · 2024Article
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Authors and funding
10 authors.
Funding
Abstract
objectiveTo develop and validate an approach that identifies patients eligible for lung cancer screening (LCS) by combining structured and unstructured smoking data from the electronic health record (EHR).
methodsWe identified patients aged 50-80 years who had at least one encounter in a primary care clinic at Vanderbilt University Medical Center (VUMC) between 2019 and 2022. We fine-tuned an existing natural language processing (NLP) tool to extract quantitative smoking information using clinical notes collected from VUMC. Then, we developed an approach to identify patients who are eligible for LCS by combining smoking information from structured data and clinical narratives. We compared this method with two approaches to identify LCS eligibility only using smoking information from structured EHR. We used 50 patients with a documented history of tobacco use for comparison and validation.
results102,475 patients were included. The NLP-based approach achieved an F1-score of 0.909, and accuracy of 0.96. The baseline approach could identify 5,887 patients. Compared to the baseline approach, the number of identified patients using all structured data and the NLP-based algorithm was 7,194 (22.2 %) and 10,231 (73.8 %), respectively. The NLP-based approach identified 589 Black/African Americans, a significant increase of 119 %.
conclusionWe present a feasible NLP-based approach to identify LCS eligible patients. It provides a technical basis for the development of clinical decision support tools to potentially improve the utilization of LCS and diminish healthcare disparities.
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