2030. How Machine-Learning Technique Using Artificial Neural Network Determines Whether the Fever Is Actually Related to the Bacteremia. (26th November 2018)

• Record Type: Journal Article
• Authors: Lee, Kyoung Hwa; Yoo, Seul Gi; Kwon, Da Eun; Park, Soon Young; Dong, Jae June; Chae, Myunghun; Min, Choongki; Kang, Jaewoo; Song, Young Goo
• Is Part Of: Open forum infectious diseases. Volume 5(2018)Supplement 1
• Page Start: S591
• Page End: S591
• Publication Date: 2018-11-26
• Subjects: Communicable diseases -- Periodicals; Medical microbiology -- Periodicals; Infection -- Periodicals; 616.9
• DOI: 10.1093/ofid/ofy210.1686
• Languages: English
• ISSNs: 2328-8957

Abstract

Abstract: Background: By 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. Methods: We 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. Results: A 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. Conclusion: Compared 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.