ArticleAmerican journal of surgery2018
Seeing the forest beyond the trees: Predicting survival in burn patients with machine learning.
Article in American journal of surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Exploring gene expression heterogeneity in burn wounds through machine learning models:Annals of medicine and surgery (2012) · 2025Article
- Review
- A Survival Prediction Model of Self-Immolation Based on Machine Learning Techniques.Advanced biomedical research · 2024Article
- The effect of well-known burn-related features on machine learning algorithms in burn patients' mortality prediction.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2023Article
- The Use of Machine Learning for Predicting Complications of Free-Flap Head and Neck Reconstruction.Annals of surgical oncology · 2023Review
- Construction of an auxiliary scoring model for myelosuppression in patients with lung cancer chemotherapy based on random forest algorithm.American journal of translational research · 2023Article
- Prediction of Prognosis in Patients with Trauma by Using Machine Learning.Medicina (Kaunas, Lithuania) · 2022Article
- Prediction of Mortality after Burn Surgery in Critically Ill Burn Patients Using Machine Learning Models.Journal of personalized medicine · 2022Article
- Machine Learning Demonstrates High Accuracy for Disease Diagnosis and Prognosis in Plastic Surgery.Plastic and reconstructive surgery. Global open · 2021Article
- Routine Hematological Parameters May Be Predictors of COVID-19 Severity.Frontiers in medicine · 2021Article
- Artificial intelligence in the management and treatment of burns: a systematic review.Burns & trauma · 2021Article
- Nutrition in times of Covid-19, how to trust the deluge of scientific information.Current opinion in clinical nutrition and metabolic care · 2020Review
- Predicting in-hospital mortality of patients with febrile neutropenia using machine learning models.International journal of medical informatics · 2020Article
- Put Me in the Game Coach! Resident Participation in High-risk Surgery in the Era of Big Data.The Journal of surgical research · 2018Article
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7 authors.
Funding
Abstract
backgroundThis study aims to identify predictors of survival for burn patients at the patient and hospital level using machine learning techniques.
methodsThe HCUP SID for California, Florida and New York were used to identify patients admitted with a burn diagnosis and merged with hospital data from the AHA Annual Survey. Random forest and stochastic gradient boosting (SGB) were used to identify predictors of survival at the patient and hospital level from the top performing model.
resultsWe analyzed 31,350 patients from 670 hospitals. SGB (AUC 0.93) and random forest (AUC 0.82) best identified patient factors such as age and absence of renal failure (p < 0.001) and hospital factors such as full time residents (p < 0.001) and nurses (p = 0.004) to be associated with increased survival.
conclusionsPatient and hospital factors are predictive of survival in burn patients. It is difficult to control patient factors, but hospital factors can inform decisions about where burn patients should be treated.
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