ArticleJournal of medical Internet research2020
Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 55 citations in OpenAlex.
- Applications of machine learning approaches for pediatric asthma exacerbation management: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- AI for predicting exacerbations in KIDs with asthma (AIRE-KIDS).NPJ digital medicine · 2026Article
- Cohort vs case-control design for transformer-based prediction of asthma exacerbations in mild asthma.NPJ digital medicine · 2026Article
- Multimodal EHR-Based Prediction of Pediatric Asthma Exacerbations.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Machine learning models incorporating genotype and ancestry improve severe asthma risk prediction.Scientific reports · 2025Article
- Article
- An Updated Systematic Review on Asthma Exacerbation Risk Prediction Models Between 2017 and 2023: Risk of Bias and Applicability.Journal of asthma and allergy · 2025Review
- A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection.JMIR medical informatics · 2024Article
- Asthma prediction via affinity graph enhanced classifier: a machine learning approach based on routine blood biomarkers.Journal of translational medicine · 2024Article
- Primary Care Asthma Attack Prediction Models for Adults: A Systematic Review of Reported Methodologies and Outcomes.Journal of asthma and allergy · 2024Review
- Article
- The added value of text from Dutch general practitioner notes in predictive modeling.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review.Journal of healthcare informatics research · 2023Article
- Article
- Development of an Asthma Exacerbation Risk Prediction Model for Conversational Use by Adults in England.Pragmatic and observational research · 2023Article
- Error and Timeliness Analysis for Using Machine Learning to Predict Asthma Hospital Visits: Retrospective Cohort Study.JMIR medical informatics · 2022Article
- Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data.The Lancet. Digital health · 2022Article
- Pretrained transformer framework on pediatric claims data for population specific tasks.Scientific reports · 2022Article
- A Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma.JMIR medical informatics · 2022Article
- Ranking Rule-Based Automatic Explanations for Machine Learning Predictions on Asthma Hospital Encounters in Patients With Asthma: Retrospective Cohort Study.JMIR medical informatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundAsthma exacerbation is an acute or subacute episode of progressive worsening of asthma symptoms and can have a significant impact on patients' quality of life. However, efficient methods that can help identify personalized risk factors and make early predictions are lacking.
objectiveThis study aims to use advanced deep learning models to better predict the risk of asthma exacerbations and to explore potential risk factors involved in progressive asthma.
methodsWe proposed a novel time-sensitive, attentive neural network to predict asthma exacerbation using clinical variables from large electronic health records. The clinical variables were collected from the Cerner Health Facts database between 1992 and 2015, including 31,433 adult patients with asthma. Interpretations on both patient and cohort levels were investigated based on the model parameters.
resultsThe proposed model obtained an area under the curve value of 0.7003 through a five-fold cross-validation, which outperformed the baseline methods. The results also demonstrated that the addition of elapsed time embeddings considerably improved the prediction performance. Further analysis observed diverse distributions of contributing factors across patients as well as some possible cohort-level risk factors, which could be found supporting evidence from peer-reviewed literature such as respiratory diseases and esophageal reflux.
conclusionsThe proposed neural network model performed better than previous methods for the prediction of asthma exacerbation. We believe that personalized risk scores and analyses of contributing factors can help clinicians better assess the individual's level of disease progression and afford the opportunity to adjust treatment, prevent exacerbation, and improve outcomes.
Indexed as
Identifiers
What 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.