ArticleJournal of clinical medicine2019
Mortality Prediction of Septic Patients in the Emergency Department Based on Machine Learning.
Article in Journal of clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 5 of them syntheses 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
32 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Evaluating Artificial Intelligence for Sepsis Prediction in Emergency Departments: A Systematic Review and Meta Analysis.Journal of medical systems · 2026Pooled it
- Prediction models for mortality in patients with sepsis: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Sepsis mortality prediction using machine learning and deep learning - a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Machine learning techniques for mortality prediction in emergency departments: a systematic review.BMJ open · 2021Pooled it
- A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis.PloS one · 2021Trial
- Personalized sepsis mortality prediction: An interpretable machine learning nomogram.Clinics (Sao Paulo, Brazil) · 2026Article
- In-hospital survival characteristics and predictive model for patients with malignant tumors and sepsis.Frontiers in medicine · 2026Article
- Review
- The role of artificial intelligence in sepsis in the Emergency Department: a narrative review.Annals of translational medicine · 2025Review
- Advancements in Artificial Intelligence in Emergency Medicine in Taiwan: A Narrative Review.Journal of acute medicine · 2024Article
- Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department.Scandinavian journal of trauma, resuscitation and emergency medicine · 2024Article
- Deep learning-based prediction of in-hospital mortality for sepsis.Scientific reports · 2024Article
- Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients.Medicina (Kaunas, Lithuania) · 2023Article
- Sepsis Prediction by Using a Hybrid Metaheuristic Algorithm: A Novel Approach for Optimizing Deep Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review.Archives of academic emergency medicine · 2023Article
- Predicting Abnormalities in Laboratory Values of Patients in the Intensive Care Unit Using Different Deep Learning Models: Comparative Study.JMIR medical informatics · 2022Article
- Sepsis Mortality Prediction Using Wearable Monitoring in Low-Middle Income Countries.Sensors (Basel, Switzerland) · 2022Article
- Predicting Sepsis Mortality in a Population-Based National Database: Machine Learning Approach.Journal of medical Internet research · 2022Article
- Early warning model for death of sepsis via length insensitive temporal convolutional network.Medical & biological engineering & computing · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
In emergency departments, the most common cause of death associated with suspected infected patients is sepsis. In this study, deep learning algorithms were used to predict the mortality of suspected infected patients in a hospital emergency department. During January 2007 and December 2013, 42,220 patients considered in this study were admitted to the emergency department due to suspected infection. In the present study, a deep learning structure for mortality prediction of septic patients was developed and compared with several machine learning methods as well as two sepsis screening tools: the systemic inflammatory response syndrome (SIRS) and quick sepsis-related organ failure assessment (qSOFA). The mortality predictions were explored for septic patients who died within 72 h and 28 days. Results demonstrated that the accuracy rate of deep learning methods, especially Convolutional Neural Network plus SoftMax (87.01% in 72 h and 81.59% in 28 d), exceeds that of the other machine learning methods, SIRS, and qSOFA. We expect that deep learning can effectively assist medical staff in early identification of critical patients.
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.