Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Academic emergency medicine : official journal of the Society for Academic Emergency Medicine

BACKGROUND : Artificial intelligence of things (AIoT) may be a solution for predicting adverse outcomes in emergency department (ED) patients with pneumonia; however, this issue remains unclear. Therefore, we conducted this study to clarify it.

METHODS : We identified 52,626 adult ED patients with pneumonia from three hospitals between 2010 and 2019 for this study. Thirty-three feature variables from electronic medical records were used to construct an artificial intelligence (AI) model to predict sepsis or septic shock, respiratory failure, and mortality. After comparisons of the predictive accuracies among logistic regression, random forest, support-vector machine (SVM), light gradient boosting machine (LightGBM), multilayer perceptron (MLP), and eXtreme Gradient Boosting (XGBoost), we selected the best one to build the model. We further combined the AI model with the Internet of things as AIoT, added an interactive mode, and implemented it in the hospital information system to assist clinicians with decision making in real time. We also compared the AIoT-based model with the confusion-urea-respiratory rate-blood pressure-65 (CURB-65) and pneumonia severity index (PSI) for predicting mortality.

RESULTS : The best AI algorithms were random forest for sepsis or septic shock (area under the curve [AUC] = 0.781), LightGBM for respiratory failure (AUC = 0.847), and mortality (AUC = 0.835). The AIoT-based model represented better performance than CURB-65 and PSI indicators for predicting mortality (0.835 vs. 0.681 and 0.835 vs. 0.728).

CONCLUSIONS : A real-time interactive AIoT-based model might be a better tool for predicting adverse outcomes in ED patients with pneumonia. Further validation in other populations is warranted.

Chen You-Ming, Kao Yuan, Hsu Chien-Chin, Chen Chia-Jung, Ma Yu-Shan, Shen Yu-Ting, Liu Tzu-Lan, Hsu Shu-Lien, Lin Hung-Jung, Wang Jhi-Joung, Huang Chien-Cheng, Liu Chung-Feng

2021-Jul-29

adverse outcome, artificial intelligence, emergency department, interactive, internet of things, pneumonia