ArticleJournal of the American Medical Informatics Association : JAMIA
Identifying patient smoking status from medical discharge records.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 190 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
190 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Can antiepileptic efficacy and epilepsy variables be studied from electronic health records? A review of current approaches.Seizure · 2021Pooled it
- Evaluating the state of the art in coreference resolution for electronic medical records.Journal of the American Medical Informatics Association : JAMIAPooled it
- A study of large language models for patient information extraction: Model architecture, fine-tuning strategy, and multi-task instruction tuning.Journal of biomedical informatics · 2026Article
- Information extraction from clinical notes: are we ready to switch to large language models?Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Lightweight open-source large language models versus cTAKES for information extraction from discharge summaries: tobacco smoking status test case.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
- SmokeBERT and Beyond: Bridging Clinical Narratives and Structured Smoking Data to Improve Lung Cancer Screening.JCO clinical cancer informatics · 2025Article
- Improving social determinants of health documentation in French electronic health records using large language models.Scientific reports · 2025Article
- Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2025Article
- Keyword-optimized template insertion for clinical note classification via prompt-based learning.BMC medical informatics and decision making · 2025Article
- Question Answering for Electronic Health Records: Scoping Review of Datasets and Models.Journal of medical Internet research · 2024Article
- Identification of patients' smoking status using an explainable AI approach: a Danish electronic health records case study.BMC medical research methodology · 2024Article
- Secondary Use of Clinical Problem List Descriptions for Bi-Encoder Based ICD-10 Classification.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Natural Language Processing for Radiation Oncology: Personalizing Treatment Pathways.Pharmacogenomics and personalized medicine · 2024Review
- Impact of possible errors in natural language processing-derived data on downstream epidemiologic analysis.JAMIA open · 2023Article
- Combining unsupervised, supervised and rule-based learning: the case of detecting patient allergies in electronic health records.BMC medical informatics and decision making · 2023Article
- Leveraging natural language processing to augment structured social determinants of health data in the electronic health record.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- The 2022 n2c2/UW shared task on extracting social determinants of health.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Automated identification of eviction status from electronic health record notes.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Heart disease risk factors detection from electronic health records using advanced NLP and deep learning techniques.Scientific reports · 2023Article
130 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The authors organized a Natural Language Processing (NLP) challenge on automatically determining the smoking status of patients from information found in their discharge records. This challenge was issued as a part of the i2b2 (Informatics for Integrating Biology to the Bedside) project, to survey, facilitate, and examine studies in medical language understanding for clinical narratives. This article describes the smoking challenge, details the data and the annotation process, explains the evaluation metrics, discusses the characteristics of the systems developed for the challenge, presents an analysis of the results of received system runs, draws conclusions about the state of the art, and identifies directions for future research. A total of 11 teams participated in the smoking challenge. Each team submitted up to three system runs, providing a total of 23 submissions. The submitted system runs were evaluated with microaveraged and macroaveraged precision, recall, and F-measure. The systems submitted to the smoking challenge represented a variety of machine learning and rule-based algorithms. Despite the differences in their approaches to smoking status identification, many of these systems provided good results. There were 12 system runs with microaveraged F-measures above 0.84. Analysis of the results highlighted the fact that discharge summaries express smoking status using a limited number of textual features (e.g., "smok", "tobac", "cigar", Social History, etc.). Many of the effective smoking status identifiers benefit from these features.
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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.