We introduce a Feasible Artificial Intelligence with Simple Trajectories for Predicting Adverse Catastrophic Events (FAST-PACE) solution for preparing immediate intervention in emergency situations. FAST-PACE utilizes a concise set of collected features to construct an artificial intelligence model that predicts the onset of cardiac arrest or acute respiratory failure from 1 h to 6 h prior to its occurrence. Data from the trajectory of 29,181 patients in intensive care units of two hospitals includes periodic vital signs, a history of treatment, current health status, and recent surgery. It excludes the results of laboratory data to construct a feasible application in wards, out-hospital emergency care, emergency transport, or other clinical situations where instant medical decisions are required with restricted patient data. These results are superior to previous warning scores including the Modified Early Warning Score (MEWS) and the National Early Warning Score (NEWS). The primary outcome was the feasibility of an artificial intelligence (AI) model predicting adverse events 1 h to 6 h prior to occurrence without lab data; the area under the receiver operating characteristic curve of this model was 0.886 for cardiac arrest and 0.869 for respiratory failure 6 h before occurrence. The secondary outcome was the superior prediction performance to MEWS (net reclassification improvement of 0.507 for predicting cardiac arrest and 0.341 for predicting respiratory failure) and NEWS (net reclassification improvement of 0.412 for predicting cardiac arrest and 0.215 for predicting respiratory failure) 6 h before occurrence. This study suggests that AI consisting of simple vital signs and a brief interview could predict a cardiac arrest or acute respiratory failure 6 h earlier.
BackgroundBy applying machine-learning-based algorithm using artificial intelligence to massive medical data, we are trying to build a real-time monitoring system for prediction of diseases to support accurate and efficient clinical decision making in time. In the previous study, we presented a model for predicting bacteremia using Bayesian statistical approach. Now, we have developed various machine-learning technique-based prediction model to achieve better prediction performance.MethodsWe retrospectively analyzed 13,402 febrile patients who were admitted to Gangnam Severance Hospital, a tertiary center in Seoul, South Korea. The training data were 11,061 patients with admission date from July 2008 to August 2011, and validation data were 2,341 patients from September 2011 to February 2012. The primary outcome was bacteremia, and the training data were analyzed to make prediction model with conventional Bayesian approach, Support Vector Machine (SVM), Random Forest (RF) and multi-layer perceptron (MLP), a representative artificial neural network (ANN) model, respectively. The performance of prediction was assessed based on the area under the curve (AUC) and sensitivity from validation data. We used 20 clinical variables for predictors of bacteremia same as Bayesian approach. The difference from the previous model was that each variable had been stratified, but in this study, they were trained by continuous number as it is.ResultsA total of 1,538 bacteremia episodes were identified from 13,402 febrile patients. The AUC of bacteremia prediction performance in SVM model was lowest with the result of 0.699 (95%CI; 0.687–0.700), even though it was 0.7 in conventional Bayesian statistical method. The highest results were 0.732 (95% CI; 0.722–0.733) in RF model and in MLP with 128 nodes of hidden layer model, the AUC was 0.719 (95% CI; 0.712–0.728) and in MLP with 256 nodes, it was 0.727 (95% CI; 0.713–0.727). In comparison with sensitivity, MLP models (0.810, 95% CI 0.772–0.747 in 128 nodes, 0.810, 95% CI, 0.782–0.837 in 256 nodes) were the highest but in RF model, the sensitivity was the lowest.ConclusionCompared with conventional statistical model, ANN-based bacteremia prediction model-MLP showed better predictive value. In order to improve the performance of prediction, further larger amount of clinical data is needed to be analyzed.Disclosures All authors: No reported disclosures.
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