Trial reportScientific reports2020
Novel application of an automated-machine learning development tool for predicting burn sepsis: proof of concept.
Trial report in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 21 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.
Surgical Critical Care Initiative (SC2i) Tissue and Data Acquisition Protocol (TDAP) in Burn Patients Improving the Robustness and Generalizability of Post-Burn Sepsis Prediction With the Post-Burn Sepsis Digital Twin
Surgical Critical Care Initiative (SC2i) Tissue and Data Acquisition Protocol (TDAP) in Burn Patients Improving the Robustness and Generalizability of Post-burn Sepsis Prediction With the Post-Burn Sepsis Digital Twin. Group 1
Who cites it
21 citing papers in PubMed, 46 citations in OpenAlex.
- Infections in Burn Patients: Pathophysiology, Prevention, and Contemporary Therapeutic Strategies in the Era of Antimicrobial Resistance.Infectious diseases and therapy · 2026Review
- [Current applications and prospects of artificial intelligence in burn diagnosis and treatment].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2026Review
- The role of artificial intelligence in burn assessment, complication diagnosis, and outcome prediction: a narrative review.Burns & trauma · 2026Review
- Simultaneous prediction of early and delayed mortality in burn patients: a comparative machine learning analysis of feature importance in a single-center retrospective study.BMC medical informatics and decision making · 2025Article
- Review
- Development and validation of web-based, interpretable predictive models for sepsis and mortality in extensive burns.Frontiers in cellular and infection microbiology · 2025Article
- Comparing machine learning screening approaches using clinical data and cytokine profiles for COVID-19 in resource-limited and resource-abundant settings.Scientific reports · 2024Article
- An Overview of Recent Developments in the Management of Burn Injuries.International journal of molecular sciences · 2023Review
- Common statistical concepts in the supervised Machine Learning arena.Frontiers in oncology · 2023Review
- Prediction of Mortality after Burn Surgery in Critically Ill Burn Patients Using Machine Learning Models.Journal of personalized medicine · 2022Article
- Effectiveness of automated alerting system compared to usual care for the management of sepsis.NPJ digital medicine · 2022Article
- Comparative performance of two automated machine learning platforms for COVID-19 detection by MALDI-TOF-MS.PloS one · 2022Article
- A bibliometric analysis of publications on burn sepsis using VOSviewer.Frontiers in medicine · 2022Article
- Prediction of Tuberculosis Using an Automated Machine Learning Platform for Models Trained on Synthetic Data.Journal of pathology informatics · 2022Article
- Evolving Applications of Artificial Intelligence and Machine Learning in Infectious Diseases Testing.Clinical chemistry · 2021Review
- Automated machine learning for endemic active tuberculosis prediction from multiplex serological data.Scientific reports · 2021Article
- Longitudinal profiling of the burn patient cutaneous and gastrointestinal microbiota: a pilot study.Scientific reports · 2021Article
- Novel application of automated machine learning with MALDI-TOF-MS for rapid high-throughput screening of COVID-19: a proof of concept.Scientific reports · 2021Article
- Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective.Frontiers in medicine · 2021Article
- Artificial intelligence in the management and treatment of burns: a systematic review.Burns & trauma · 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
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis is the primary cause of burn-related mortality and morbidity. Traditional indicators of sepsis exhibit poor performance when used in this unique population due to their underlying hypermetabolic and inflammatory response following burn injury. To address this challenge, we developed the Machine Intelligence Learning Optimizer (MILO), an automated machine learning (ML) platform, to automatically produce ML models for predicting burn sepsis. We conducted a retrospective analysis of 211 adult patients (age ≥ 18 years) with severe burn injury (≥ 20% total body surface area) to generate training and test datasets for ML applications. The MILO approach was compared against an exhaustive "non-automated" ML approach as well as standard statistical methods. For this study, traditional multivariate logistic regression (LR) identified seven predictors of burn sepsis when controlled for age and burn size (OR 2.8, 95% CI 1.99-4.04, P = 0.032). The area under the ROC (ROC-AUC) when using these seven predictors was 0.88. Next, the non-automated ML approach produced an optimal model based on LR using 16 out of the 23 features from the study dataset. Model accuracy was 86% with ROC-AUC of 0.96. In contrast, MILO identified a k-nearest neighbor-based model using only five features to be the best performer with an accuracy of 90% and a ROC-AUC of 0.96. Machine learning augments burn sepsis prediction. MILO identified models more quickly, with less required features, and found to be analytically superior to traditional ML approaches. Future studies are needed to clinically validate the performance of MILO-derived ML models for sepsis prediction.
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.