ArticleCancer informatics2016
Comparison of Three Information Sources for Smoking Information in Electronic Health Records.
Article in Cancer informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The validity of electronic health data for measuring smoking status: a systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled 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
- Training a Smoking Status Probabilistic Model Using Cotinine Levels in a Large Claims Database.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2026Article
- Review
- RELEAP: reinforcement-enhanced label-efficient active phenotyping for electronic health records.JAMIA open · 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
- Understanding Patient Perspectives on Digital Smoking History Data Collection.CHEST pulmonary · 2025Article
- Using Structured Codes and Free-Text Notes to Measure Information Complementarity in Electronic Health Records: Feasibility and Validation Study.Journal of medical Internet research · 2025Article
- Efficacy of Digital Outreach Strategies for Collecting Smoking Data: Pragmatic Randomized Trial.JMIR formative research · 2024Article
- Large Language Models for Efficient Medical Information Extraction.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Systematic replication of smoking disease associations using survey responses and EHR data in the All of Us Research Program.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- A predictive algorithm to identify ever smoking in medical claims-based epidemiologic studies.Annals of epidemiology · 2023Article
- Development of an Electronic Health Record-Based Algorithm for Predicting Lung Cancer Screening Eligibility in the Population-Based Research to Optimize the Screening Process Lung Research Consortium.JCO clinical cancer informatics · 2023Article
- Leveraging natural language processing to identify eligible lung cancer screening patients with the electronic health record.International journal of medical informatics · 2023Article
- Assessing longitudinal housing status using Electronic Health Record data: a comparison of natural language processing, structured data, and patient-reported history.Frontiers in artificial intelligence · 2023Article
- Automated Detection of Substance-Use Status and Related Information from Clinical Text.Sensors (Basel, Switzerland) · 2022Article
- Real-world efficacy of osimertinib in previously EGFR-TKI treated NSCLC patients without identification of T790M mutation.Journal of cancer research and clinical oncology · 2022Article
- Using the biomarker cotinine and survey self-report to validate smoking data from United States Veterans Health Administration electronic health records.JAMIA open · 2022Article
- Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing.JCO clinical cancer informatics · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectiveThe primary aim was to compare independent and joint performance of retrieving smoking status through different sources, including narrative text processed by natural language processing (NLP), patient-provided information (PPI), and diagnosis codes (ie, International Classification of Diseases, Ninth Revision [ICD-9]). We also compared the performance of retrieving smoking strength information (ie, heavy/light smoker) from narrative text and PPI. MATERIALS AND
methodsOur study leveraged an existing lung cancer cohort for smoking status, amount, and strength information, which was manually chart-reviewed. On the NLP side, smoking-related electronic medical record (EMR) data were retrieved first. A pattern-based smoking information extraction module was then implemented to extract smoking-related information. After that, heuristic rules were used to obtain smoking status-related information. Smoking information was also obtained from structured data sources based on diagnosis codes and PPI. Sensitivity, specificity, and accuracy were measured using patients with coverage (ie, the proportion of patients whose smoking status/strength can be effectively determined).
resultsNLP alone has the best overall performance for smoking status extraction (patient coverage: 0.88; sensitivity: 0.97; specificity: 0.70; accuracy: 0.88); combining PPI with NLP further improved patient coverage to 0.96. ICD-9 does not provide additional improvement to NLP and its combination with PPI. For smoking strength, combining NLP with PPI has slight improvement over NLP alone.
conclusionThese findings suggest that narrative text could serve as a more reliable and comprehensive source for obtaining smoking-related information than structured data sources. PPI, the readily available structured data, could be used as a complementary source for more comprehensive patient coverage.
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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.