ArticleJournal of personalized medicine2022
Novel Machine Learning Approach to Predict and Personalize Length of Stay for Patients Admitted with Syncope from the Emergency Department.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Models to predict length of stay in the emergency department: a systematic literature review and appraisal.BMC emergency medicine · 2024Pooled it
- Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- The hope and the hype of artificial intelligence for syncope management.European heart journal. Digital health · 2025Article
- AI-driven healthcare innovations for enhancing clinical services during mass gatherings (Hajj): task force insights and future directions.BMC health services research · 2025Article
- Development of Multiservice Machine Learning Models to Predict Postsurgical Length of Stay and Discharge Disposition at the Time of Case Posting.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2025Article
- Developing practical machine learning survival models to identify high-risk patients for in-hospital mortality following traumatic brain injury.Scientific reports · 2025Article
- Machine Learning Methods for Predicting Syncope Severity in the Emergency Department: A Retrospective Analysis.Health science reports · 2025Article
- Will Artificial Intelligence Be "Better" Than Humans in the Management of Syncope?JACC. Advances · 2024Review
- Use of Artificial Intelligence in Triage in Hospital Emergency Departments: A Scoping Review.Cureus · 2024Article
- A Hybrid Model for 30-Day Syncope Prognosis Prediction in the Emergency Department.Journal of personalized medicine · 2023Article
- Can Artificial Intelligence Enhance Syncope Management?: AJACC. Advances · 2023Review
- Syncope-Do We Need AI?Journal of personalized medicine · 2023Article
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Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Syncope, a common problem encountered in the emergency department (ED), has a multitude of causes ranging from benign to life-threatening. Hospitalization may be required, but the management can vary substantially depending on specific clinical characteristics. Models predicting admission and hospitalization length of stay (LoS) are lacking. The purpose of this study was to design an effective, exploratory model using machine learning (ML) technology to predict LoS for patients presenting with syncope. Methods: This was a retrospective analysis using over 4 million patients from the National Emergency Department Sample (NEDS) database presenting to the ED with syncope between 2016−2019. A multilayer perceptron neural network with one hidden layer was trained and validated on this data set. Results: Receiver Operator Characteristics (ROC) were determined for each of the five ANN models with varying cutoffs for LoS. A fair area under the curve (AUC of 0.78) to good (AUC of 0.88) prediction performance was achieved based on sequential analysis at different cutoff points, starting from the same day discharge and ending at the longest analyzed cutoff LoS ≤7 days versus >7 days, accordingly. The ML algorithm showed significant sensitivity and specificity in predicting short (≤48 h) versus long (>48 h) LoS, with an AUC of 0.81. Conclusions: Using variables available to triaging ED clinicians, ML shows promise in predicting hospital LoS with fair to good performance for patients presenting with syncope.
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