Machine Learning-Based Text Analysis to Predict Severely Injured Patients in Emergency Medical Dispatch: Model Development and Validation

Chin, Kuan-ChenKuan-ChenChinCheng, Yu-ChiaYu-ChiaChengSun, Jen-TangJen-TangSunOu, Chih-YenChih-YenOuHu, Chun-HuaChun-HuaHuTsai, Ming-ChiMing-ChiTsaiMATTHEW HUEI-MING MAWEN-CHU CHIANGALBERT CHEN2022-07-292022-07-2920221438-8871https://scholars.lib.ntu.edu.tw/handle/123456789/616010Early recognition of severely injured patients in prehospital settings is of paramount importance for timely treatment and transportation of patients to further treatment facilities. The dispatching accuracy has seldom been addressed in previous studies.enBernoulli naïve Bayes; dispatcher; emergency medical dispatch; emergency medical service; frequency–inverse document frequency; machine learning; trauma[SDGs]SDG3Machine Learning-Based Text Analysis to Predict Severely Injured Patients in Emergency Medical Dispatch: Model Development and Validationjournal article10.2196/30210356873932-s2.0-85131902526https://api.elsevier.com/content/abstract/scopus_id/85131902526