Prediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population
2.2. Data Extraction
2.3. Definition of Bacteremia
2.4. Clinical Variables
2.5. Subgroup Analysis
2.6. Machine Learning Technique and Statistical Analysis
2.7. Ethics Approval and Consent to Participate
3. Results
3.1. Comprehensive Analysis
3.2. Influence Ranking of Clinical Variables
3.3. Type of Pathogen
3.4. Source of Infection
3.5. Age and Sex Subgroup Analyses
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Type of Data | Model | AUROC (95% CI) | Sensitivity | Specificity |
---|---|---|---|---|
12 h | MLP | 0.762 (0.7617–0.7623) | 0.695 | 0.706 |
Random Forest | 0.758 (0.7572–0.7591) | 0.664 | 0.723 | |
XGBoost (Gbtree) | 0.745 (0.7446–0.7455) | 0.629 | 0.747 | |
XGBoost (DART) | 0.744 (0.7439–0.7446) | 0.638 | 0.747 | |
24 h | MLP | 0.753 (0.7520–0.7529) | 0.602 | 0.730 |
Random Forest | 0.738 (0.7383–0.7401) | 0.643 | 0.729 | |
XGBoost (Gbtree) | 0.730 (0.7300–0.7304) | 0.607 | 0.729 | |
XGBoost (DART) | 0.727 (0.7256–0.7275) | 0.602 | 0.702 |
Rank | Data Fusion within 12-h | Data Fusion within 24-h |
---|---|---|
1 | Monocyte | Monocyte |
2 | Platelet | Neutrophil |
3 | Hospital stay * | Platelet |
4 | Neutrophil | Albumin |
5 | T. bilirubin | ALP |
6 | BUN | T. bilirubin |
7 | Albumin | tCO2 |
8 | tCO2 | BUN |
9 | AST | Hospital stay * |
10 | ALP | CRP |
11 | ALT | Total Protein |
12 | White blood cell count | Creatinine |
13 | Chloride | ALT |
14 | aPTT | Pulse rate |
15 | Total Protein | Prothrombin time |
16 | Pulse rate | Hemoglobin |
17 | Respiratory rate | AST |
18 | DBP | Sodium |
19 | Creatinine | Chloride |
20 | CRP | ESR |
Type of Data | Subgroup | With Bacteremia | Without Bacteremia | AUROC (95% CI) | Sensitivity | Specificity | |
---|---|---|---|---|---|---|---|
12 h | Pathogen | E. coli | 1805 | 14,068 | 0.794 (0.7928–0.7946) | 0.693 | 0.766 |
S. aureus | 1827 | 14,068 | 0.672 (0.6717–0.6741) | 0.656 | 0.618 | ||
K. pneumoniae | 1518 | 14,068 | 0.763 (0.7616–0.7658) | 0.677 | 0.716 | ||
A. baumannii | 1727 | 14,068 | 0.839 (0.8388–0.8394) | 0.789 | 0.750 | ||
P. aeruginosa | 855 | 14,068 | 0.729 (0.7278–0.7331) | 0.611 | 0.706 | ||
Infection site | Urine | 2202 | 21,540 | 0.749 (0.7485–0.7504) | 0.642 | 0.725 | |
Sputum | 3051 | 21,540 | 0.822 (0.8217–0.8229) | 0.792 | 0.715 | ||
Bile | 650 | 21,540 | 0.775 (0.7742–0.7764) | 0.739 | 0.684 | ||
Primary bacteremia | 557 | 21,540 | 0.561 (0.5572–0.5651) | 0.636 | 0.473 | ||
Age | 18–39 years | 1377 | 3885 | 0.781 (0.7780–0.7868) | 0.3923 | 0.8898 | |
40–59 years | 5141 | 8359 | 0.718 (0.7152–0.7190) | 0.5818 | 0.7382 | ||
60–80 years | 8936 | 14,862 | 0.761 (0.7594–0.7622) | 0.7405 | 0.6597 | ||
Sex | Male | 9416 | 117,535 | 0.763 (0.7630–0.7636) | 0.710 | 0.694 | |
Female | 6038 | 81,236 | 0.759 (0.7586–0.7600) | 0.670 | 0.724 | ||
24 h | Pathogen | E. coli | 2771 | 22,114 | 0.753 (0.7523–0.7353) | 0.639 | 0.738 |
S. aureus | 2949 | 22,114 | 0.737 (0.7354–0.7376) | 0.668 | 0.684 | ||
K. pneumoniae | 2376 | 22,114 | 0.778 (0.7777–0.7795) | 0.706 | 0.730 | ||
A. baumannii | 2621 | 22,114 | 0.840 (0.8400–0.8407) | 0.817 | 0.747 | ||
P. aeruginosa | 1280 | 22,114 | 0.718 (0.7170–0.7209) | 0.688 | 0.661 | ||
Infection site | Urine | 3489 | 34,502 | 0.787 (0.7860–0.7878) | 0.751 | 0.689 | |
Sputum | 4700 | 34,502 | 0.805 (0.8041–0.8052) | 0.670 | 0.756 | ||
Bile | 1022 | 34,502 | 0.792 (0.7910–0.7936) | 0.659 | 0.740 | ||
Primary bacteremia | 987 | 7169 | 0.583 (0.5823–0.5855) | 0.517 | 0.616 | ||
Age | 18–39 years | 413 | 5712 | 0.758 (0.7554–0.7648) | 0.463 | 0.845 | |
40–59 years | 1415 | 12,415 | 0.719 (0.7176–0.7214) | 0.566 | 0.749 | ||
60–80 years | 3026 | 21,154 | 0.723 (0.7221–0.7238) | 0.560 | 0.713 | ||
Sex | Male | 14,585 | 241,478 | 0.748 (0.7473–0.7481) | 0.673 | 0.707 | |
Female | 10,030 | 164,521 | 0.760 (0.7597–0.7608) | 0.619 | 0.760 | ||
Merge hour | Under 12 h | 323,949 | 20,608 | 0.726 (0.7261–0.7266) | 0.631 | 0.705 | |
Over 12 h | 82,050 | 3957 | 0.726 (0.7253–0. 7258) | 0.653 | 0.679 |
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Lee, K.H.; Dong, J.J.; Kim, S.; Kim, D.; Hyun, J.H.; Chae, M.-H.; Lee, B.S.; Song, Y.G. Prediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time. Diagnostics 2022, 12, 102. https://doi.org/10.3390/diagnostics12010102
Lee KH, Dong JJ, Kim S, Kim D, Hyun JH, Chae M-H, Lee BS, Song YG. Prediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time. Diagnostics. 2022; 12(1):102. https://doi.org/10.3390/diagnostics12010102
Chicago/Turabian StyleLee, Kyoung Hwa, Jae June Dong, Subin Kim, Dayeong Kim, Jong Hoon Hyun, Myeong-Hun Chae, Byeong Soo Lee, and Young Goo Song. 2022. "Prediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time" Diagnostics 12, no. 1: 102. https://doi.org/10.3390/diagnostics12010102
APA StyleLee, K. H., Dong, J. J., Kim, S., Kim, D., Hyun, J. H., Chae, M.-H., Lee, B. S., & Song, Y. G. (2022). Prediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time. Diagnostics, 12(1), 102. https://doi.org/10.3390/diagnostics12010102