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Prediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time

Early detection of bacteremia is important to prevent antibiotic abuse. Therefore, we aimed to develop a clinically applicable bacteremia prediction model using machine learning technology. Data from two tertiary medical centers’ electronic medical records during a 12-year-period were extracted. Mul...

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Autores principales: Lee, Kyoung Hwa, Dong, Jae June, Kim, Subin, Kim, Dayeong, Hyun, Jong Hoon, Chae, Myeong-Hun, Lee, Byeong Soo, Song, Young Goo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8774637/
https://www.ncbi.nlm.nih.gov/pubmed/35054269
http://dx.doi.org/10.3390/diagnostics12010102
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author Lee, Kyoung Hwa
Dong, Jae June
Kim, Subin
Kim, Dayeong
Hyun, Jong Hoon
Chae, Myeong-Hun
Lee, Byeong Soo
Song, Young Goo
author_facet Lee, Kyoung Hwa
Dong, Jae June
Kim, Subin
Kim, Dayeong
Hyun, Jong Hoon
Chae, Myeong-Hun
Lee, Byeong Soo
Song, Young Goo
author_sort Lee, Kyoung Hwa
collection PubMed
description Early detection of bacteremia is important to prevent antibiotic abuse. Therefore, we aimed to develop a clinically applicable bacteremia prediction model using machine learning technology. Data from two tertiary medical centers’ electronic medical records during a 12-year-period were extracted. Multi-layer perceptron (MLP), random forest, and gradient boosting algorithms were applied for machine learning analysis. Clinical data within 12 and 24 hours of blood culture were analyzed and compared. Out of 622,771 blood cultures, 38,752 episodes of bacteremia were identified. In MLP with 128 hidden layer nodes, the area under the receiver operating characteristic curve (AUROC) of the prediction performance in 12- and 24-h data models was 0.762 (95% confidence interval (CI); 0.7617–0.7623) and 0.753 (95% CI; 0.7520–0.7529), respectively. AUROC of causative-pathogen subgroup analysis predictive value for Acinetobacter baumannii bacteremia was the highest at 0.839 (95% CI; 0.8388–0.8394). Compared to primary bacteremia, AUROC of sepsis caused by pneumonia was highest. Predictive performance of bacteremia was superior in younger age groups. Bacteremia prediction using machine learning technology appeared possible for acute infectious diseases. This model was more suitable especially to pneumonia caused by Acinetobacter baumannii. From the 24-h blood culture data, bacteremia was predictable by substituting only the continuously variable values.
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spelling pubmed-87746372022-01-21 Prediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time Lee, Kyoung Hwa Dong, Jae June Kim, Subin Kim, Dayeong Hyun, Jong Hoon Chae, Myeong-Hun Lee, Byeong Soo Song, Young Goo Diagnostics (Basel) Article Early detection of bacteremia is important to prevent antibiotic abuse. Therefore, we aimed to develop a clinically applicable bacteremia prediction model using machine learning technology. Data from two tertiary medical centers’ electronic medical records during a 12-year-period were extracted. Multi-layer perceptron (MLP), random forest, and gradient boosting algorithms were applied for machine learning analysis. Clinical data within 12 and 24 hours of blood culture were analyzed and compared. Out of 622,771 blood cultures, 38,752 episodes of bacteremia were identified. In MLP with 128 hidden layer nodes, the area under the receiver operating characteristic curve (AUROC) of the prediction performance in 12- and 24-h data models was 0.762 (95% confidence interval (CI); 0.7617–0.7623) and 0.753 (95% CI; 0.7520–0.7529), respectively. AUROC of causative-pathogen subgroup analysis predictive value for Acinetobacter baumannii bacteremia was the highest at 0.839 (95% CI; 0.8388–0.8394). Compared to primary bacteremia, AUROC of sepsis caused by pneumonia was highest. Predictive performance of bacteremia was superior in younger age groups. Bacteremia prediction using machine learning technology appeared possible for acute infectious diseases. This model was more suitable especially to pneumonia caused by Acinetobacter baumannii. From the 24-h blood culture data, bacteremia was predictable by substituting only the continuously variable values. MDPI 2022-01-03 /pmc/articles/PMC8774637/ /pubmed/35054269 http://dx.doi.org/10.3390/diagnostics12010102 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lee, Kyoung Hwa
Dong, Jae June
Kim, Subin
Kim, Dayeong
Hyun, Jong Hoon
Chae, Myeong-Hun
Lee, Byeong Soo
Song, Young Goo
Prediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time
title Prediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time
title_full Prediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time
title_fullStr Prediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time
title_full_unstemmed Prediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time
title_short Prediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time
title_sort prediction of bacteremia based on 12-year medical data using a machine learning approach: effect of medical data by extraction time
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8774637/
https://www.ncbi.nlm.nih.gov/pubmed/35054269
http://dx.doi.org/10.3390/diagnostics12010102
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