Using machine learning to predict bacteremia in urgent care patients on the basis of triage data and laboratory results.

Autor: Chiu CP; Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 70101, Taiwan. Electronic address: ne6101131@gs.ncku.edu.tw., Chou HH; Department of Computer Science and Information Engineering, National Chi Nan University, Nantou 545301, Taiwan. Electronic address: chouhh@ncnu.edu.tw., Lin PC; Department of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan. Electronic address: pengchan@mail.ncku.edu.tw., Lee CC; Clinical Medicine Research Centre, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan; Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan. Electronic address: chichingbm85@gmail.com., Hsieh SY; Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 70101, Taiwan; Department of Computer Science and Information Engineering, National Chi Nan University, Nantou 545301, Taiwan; Institute of Manufacturing Information and Systems, National Cheng Kung University. Tainan. 70101, Taiwan; Institute of information Science, Academia Sinica, Taipei, 115, Taiwan; Research Center for Information Technology Innovation. Academia Sinica, Taipei, 115. Taiwan. Electronic address: hsiehsy@mail.ncku.edu.tw.
Jazyk: angličtina
Zdroj: The American journal of emergency medicine [Am J Emerg Med] 2024 Nov; Vol. 85, pp. 80-85. Date of Electronic Publication: 2024 Sep 02.
DOI: 10.1016/j.ajem.2024.08.045
Abstrakt: Background: Despite advancements in antimicrobial therapies, bacteremia remains a life-threatening condition. Appropriate antimicrobials must be promptly administered to ensure patient survival. However, diagnosing bacteremia based on blood cultures is time-consuming and not something emergency department (ED) personnel are routinely trained to do.
Methods: This retrospective cohort study developed several machine learning (ML) models to predict bacteremia in adults initially presenting with fever or hypothermia, comprising logistic regression, random forest, extreme gradient boosting, support vector machine, k-nearest neighbor, multilayer perceptron, and ensemble models. Random oversampling and synthetic minority oversampling techniques were adopted to balance the dataset. The variables included demographic characteristics, comorbidities, immunocompromised status, clinical characteristics, subjective symptoms reported during ED triage, and laboratory data. The study outcome was an episode of bacteremia.
Results: Of the 5063 patients with initial fever or hypothermia from whom blood cultures were obtained, 128 (2.5 %) were diagnosed with bacteremia. We combined 36 selected variables and 10 symptoms subjectively reported by patients into features for analysis in our models. The ensemble model outperformed other models, with an area under the receiver operating characteristic curve (AUROC) of 0.930 and an F1-score of 0.735. The AUROC of all models was higher than 0.80.
Conclusion: The ML models developed effectively predicted bacteremia among febrile or hypothermic patients in the ED, with all models demonstrating high AUROC values and rapid processing times. The findings suggest that ED clinicians can effectively utilize ML techniques to develop predictive models for addressing clinical challenges.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024. Published by Elsevier Inc.)
Databáze: MEDLINE
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