• You-Ming Chen, Yuan Kao, Chien-Chin Hsu, Chia-Jung Chen, Yu-Shan Ma, Yu-Ting Shen, Tzu-Lan Liu, Shu-Lien Hsu, Hung-Jung Lin, Jhi-Joung Wang, Chien-Cheng Huang, and Chung-Feng Liu.
• Department of Emergency Medicine, Chi Mei Medical Center, Tainan, Taiwan.
• Acad Emerg Med. 2021 Nov 1; 28 (11): 1277-1285.

BackgroundArtificial 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.MethodsWe 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.ResultsThe 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).ConclusionsA 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.© 2021 by the Society for Academic Emergency Medicine.

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