International journal of medical informatics
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Accurate differentiation and prioritization in emergency department (ED) triage is important to identify high-risk patients and to efficiently allocate of finite resources. Using data available from patients with suspected cardiovascular disease presenting at ED triage, this study aimed to train and compare the performance of four common machine learning models to assist in decision making of triage levels. ⋯ Four machine learning models had good discriminative ability of triage. XGBoost demonstrated a slight advantage over other models. These models could be used for differential triage of low-risk patients and high-risk patients as a strategy to improve efficiency and allocation of finite resources.
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Multicenter Study
Machine learning predicts mortality in septic patients using only routinely available ABG variables: a multi-centre evaluation.
To evaluate the application of machine learning methods, specifically Deep Neural Networks (DNN) models for intensive care (ICU) mortality prediction. The aim was to predict mortality within 96 hours after admission to mirror the clinical situation of patient evaluation after an ICU trial, which consists of 24-48 hours of ICU treatment and then "re-triage". The input variables were deliberately restricted to ABG values to maximise real-world practicability. ⋯ An LSTM-based model could help physicians with the "re-triage" and the decision to restrict treatment in patients with a poor prognosis.