Emergency Department Triage Prediction of Emergency Severity Index using Machine Learning Models

Detta är en Kandidat-uppsats från KTH/Skolan för elektroteknik och datavetenskap (EECS); KTH/Skolan för elektroteknik och datavetenskap (EECS)

Sammanfattning: Study Objective: The emergency department (ED) in the United States strongly rely on subjective assessment of patients. This study seeks to evaluate an electronic triage system based on machine learning models that can predict the patients emergency severity index (ESI). Methods: A dataset containing 560 486 patients triage data was investigated.Three different machine learning models was tested and evaluated. A crossvalidation table and a confusion matrix was conducted from each of the models. The precision rate, recall rate and f1-score were calculated and reported. Result: The Gradient Boosting model returned an accuracy rate of 68%. The random forest model returned an accuracy rate of 66%. The Gaussian Naive Bayesmodel returned an accuracy rate of 25%. Conclusion: The model that best predicted the ESI-level is the GradientBoosting model. Further testing is needed with better computational power since we could not train our model with the whole dataset.

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