ArticleClinical and experimental emergency medicine2020
Predicting 30-day mortality of patients with pneumonia in an emergency department setting using machine-learning models.
Article in Clinical and experimental emergency medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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
16 citing papers in PubMed.
- Prediction of mortality in pneumonia patients with connective tissue disease treated with glucocorticoids or/and immunosuppressants by machine learning.Frontiers in immunology · 2023Trial
- Development and multi-center validation of a machine learning‑based prediction model for mortality in tumor-related sepsis.BMC infectious diseases · 2026Article
- Multimodal AI-based 28-day mortality prediction of pneumonia patients at ED discharge: a multicenter study.Scientific reports · 2026Article
- Artificial Intelligence Applications in Pneumonia: Diagnosis and Outcome Prediction.Current pulmonology reports · 2026Review
- Time-Series modeling for predicting mortality risk in intensive care unit patients with pulmonary inflammation.Frontiers in medicine · 2026Article
- Machine learning-based model for predicting all-cause mortality in severe pneumonia.BMJ open respiratory research · 2025Article
- Comparison of Predictive Models for Keloid Recurrence Based on Machine Learning.Journal of cosmetic dermatology · 2025Article
- Enhancing pneumonia prognosis in the emergency department: a novel machine learning approach using complete blood count and differential leukocyte count combined with CURB-65 score.BMC medical informatics and decision making · 2024Article
- Machine learning-based prediction of in-ICU mortality in pneumonia patients.Scientific reports · 2023Article
- Machine Learning Model Development and Validation for Predicting Outcome in Stage 4 Solid Cancer Patients with Septic Shock Visiting the Emergency Department: A Multi-Center, Prospective Cohort Study.Journal of clinical medicine · 2022Article
- Benchmarking emergency department prediction models with machine learning and public electronic health records.Scientific data · 2022Article
- Incorporation of Suppression of Tumorigenicity 2 into Random Survival Forests for Enhancing Prediction of Short-Term Prognosis in Community-ACQUIRED Pneumonia.Journal of clinical medicine · 2022Article
- Performance of Machine Learning Algorithms for Predicting Adverse Outcomes in Community-Acquired Pneumonia.Frontiers in bioengineering and biotechnology · 2022Article
- Evaluation and management of pleural sepsis.Respiratory medicine · 2021Review
- Calibration-Free Cuffless Blood Pressure Estimation Based on a Population With a Diverse Range of Age and Blood Pressure.Frontiers in medical technology · 2021Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to confirm the accuracy of a machine-learning-based model in predicting the 30-day mortality of patients with pneumonia and evaluating whether they were required to be admitted to the intensive care unit (ICU).
methodsThe study conducted a retrospective analysis of pneumonia patients at an emergency department (ED) in Seoul, Korea, from January 1, 2016 to December 31, 2017. Patients aged 18 years or older with a pneumonia registry designation on their electronic medical record were enrolled. We collected their demographic information, mental status, and laboratory findings. Three models were used: the pre-existing CURB-65 model, and the CURB-RF and Extensive CURB-RF models, which were machine-learning models that used a random forest algorithm. The primary outcomes were ICU admission from the ED or 30-day mortality. Receiver operating characteristic curves were constructed for the models, and the areas under these curves were compared.
resultsOut of the 1,974 pneumonia patients, 1,732 patients were eligible to be included in the study; from these, 473 patients died within 30 days or were initially admitted to the ICU from the ED. The area under receiver operating characteristic curves of CURB-65, CURB-RF, and extensive-CURB-RF were 0.615 (0.614-0.616), 0.701 (0.700-0.702), and 0.844 (0.843-0.845), respectively.
conclusionThe proposed machine-learning models could predict the mortality of patients with pneumonia more accurately than the pre-existing CURB-65 model and can help decide whether the patient should be admitted to the ICU.
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.