ArticleJournal of the American Medical Informatics Association : JAMIA
Patient-level temporal aggregation for text-based asthma status ascertainment.
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 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Validating exacerbations of asthma in electronic health records: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial.PloS one · 2021Trial
- Application of a natural language processing algorithm to early asthma ascertainment for adults in the era of electronic health records.The journal of allergy and clinical immunology. Global · 2026Article
- Temporal Annotation of German Clinical Language in Real and Synthetic Clinical Documents: Corpus Development and Baseline Tagger Validation Study.Journal of medical Internet research · 2026Article
- Association of delayed asthma diagnosis with asthma exacerbations in children.The journal of allergy and clinical immunology. Global · 2025Article
- Validating the recording of exacerbations of asthma in electronic health records: a systematic review protocol.BMJ open · 2024Article
- Artificial intelligence approaches using natural language processing to advance EHR-based clinical research.The Journal of allergy and clinical immunology · 2020Review
- Extracting Smoking Status from Electronic Health Records Using NLP and Deep Learning.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Early Identification of Childhood Asthma: The Role of Informatics in an Era of Electronic Health Records.Frontiers in pediatrics · 2019Review
- Modeling asynchronous event sequences with RNNs.Journal of biomedical informatics · 2018Article
- Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach.BMC medical informatics and decision making · 2017Article
- Application of a Natural Language Processing Algorithm to Asthma Ascertainment. An Automated Chart Review.American journal of respiratory and critical care medicine · 2017Article
- Validation of asthma recording in electronic health records: protocol for a systematic review.BMJ open · 2017Article
- Building a Natural Language Processing Tool to Identify Patients With High Clinical Suspicion for Kawasaki Disease from Emergency Department Notes.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2016Article
- Natural Language Processing for Cohort Discovery in a Discharge Prediction Model for the Neonatal ICU.Applied clinical informatics · 2016Article
- Trends in biomedical informatics: automated topic analysis of JAMIA articles.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Automated methods for the summarization of electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2015Review
- Semi-supervised Learning for Phenotyping Tasks.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2015Article
- Natural Language Processing for Asthma Ascertainment in Different Practice Settings.The journal of allergy and clinical immunology. In practiceArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectiveTo specify the problem of patient-level temporal aggregation from clinical text and introduce several probabilistic methods for addressing that problem. The patient-level perspective differs from the prevailing natural language processing (NLP) practice of evaluating at the term, event, sentence, document, or visit level.
methodsWe utilized an existing pediatric asthma cohort with manual annotations. After generating a basic feature set via standard clinical NLP methods, we introduce six methods of aggregating time-distributed features from the document level to the patient level. These aggregation methods are used to classify patients according to their asthma status in two hypothetical settings: retrospective epidemiology and clinical decision support.
resultsIn both settings, solid patient classification performance was obtained with machine learning algorithms on a number of evidence aggregation methods, with Sum aggregation obtaining the highest F1 score of 85.71% on the retrospective epidemiological setting, and a probability density function-based method obtaining the highest F1 score of 74.63% on the clinical decision support setting. Multiple techniques also estimated the diagnosis date (index date) of asthma with promising accuracy. DISCUSSION: The clinical decision support setting is a more difficult problem. We rule out some aggregation methods rather than determining the best overall aggregation method, since our preliminary data set represented a practical setting in which manually annotated data were limited.
conclusionResults contrasted the strengths of several aggregation algorithms in different settings. Multiple approaches exhibited good patient classification performance, and also predicted the timing of estimates with reasonable accuracy.
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