Novel Machine Learning Approach to Predict and Personalize Length of Stay for Patients Admitted with Syncope from the Emergency Departmen.
Sangil LeeAvinash Reddy MudireddyDeepak Kumar PasupulaMehul AdhadukE John BarsottiMilan SonkaGiselle M StatzTyler BullisSamuel L JohnstonAron Z EvansBrian OlshanskyMilena A GebskaPublished in: Journal of personalized medicine (2022)
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.
Keyphrases
- emergency department
- pulmonary embolism
- neural network
- machine learning
- end stage renal disease
- adverse drug
- big data
- chronic kidney disease
- ejection fraction
- newly diagnosed
- healthcare
- deep learning
- public health
- palliative care
- prognostic factors
- electronic health record
- quality improvement
- resistance training
- high intensity