Machine learning for early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy.
Patients after holmium laser lithotripsy have a certain probability of getting postoperative infection. An early and accurate diagnosis of postoperative infection allows a timely administration of appropriate antibiotic treatment. However, doctors can not accurately determine whether the patient has...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0317584 |
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| _version_ | 1849328809666412544 |
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| author | Weiqi Xia Miaomiao Zhang Xiaowei Zheng Zushuai Wu Zixue Xuan Ping Huang Xiuli Yang |
| author_facet | Weiqi Xia Miaomiao Zhang Xiaowei Zheng Zushuai Wu Zixue Xuan Ping Huang Xiuli Yang |
| author_sort | Weiqi Xia |
| collection | DOAJ |
| description | Patients after holmium laser lithotripsy have a certain probability of getting postoperative infection. An early and accurate diagnosis of postoperative infection allows a timely administration of appropriate antibiotic treatment. However, doctors can not accurately determine whether the patient has the infection. Here, a novel strategy is put forward to assist in predicting postoperative infection early by using machine learning methods. We retrospectively collected 1006 cases of patients with urinary stone after treatment of holmium laser lithotripsy from Zhejiang Provincial People's Hospital. Feature engineering was added to filter the important characteristics and Miceforest multiple imputation method was applied to tackle the missing data problem. We used 5-fold cross-validation to train and validate the six machine learning methods. Besides, we could also find key variables important to postoperative infection by explaining the model. The hyperparameters were constantly adjusted to achieve the best performance of model. The result showed that LR had a best performance in independent datasets with AUC of 0.734. And the SHAP values indicated that preoperative urine leukocyte count was the most important variable to the prediction. Our study enables accurate predictions of infection in urology perioperative periods, the key variables can be interpreted better and more accurately to support clinical decision making. |
| format | Article |
| id | doaj-art-77b9d271e412416bb6724fb945261a7b |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-77b9d271e412416bb6724fb945261a7b2025-08-20T03:47:28ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01205e031758410.1371/journal.pone.0317584Machine learning for early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy.Weiqi XiaMiaomiao ZhangXiaowei ZhengZushuai WuZixue XuanPing HuangXiuli YangPatients after holmium laser lithotripsy have a certain probability of getting postoperative infection. An early and accurate diagnosis of postoperative infection allows a timely administration of appropriate antibiotic treatment. However, doctors can not accurately determine whether the patient has the infection. Here, a novel strategy is put forward to assist in predicting postoperative infection early by using machine learning methods. We retrospectively collected 1006 cases of patients with urinary stone after treatment of holmium laser lithotripsy from Zhejiang Provincial People's Hospital. Feature engineering was added to filter the important characteristics and Miceforest multiple imputation method was applied to tackle the missing data problem. We used 5-fold cross-validation to train and validate the six machine learning methods. Besides, we could also find key variables important to postoperative infection by explaining the model. The hyperparameters were constantly adjusted to achieve the best performance of model. The result showed that LR had a best performance in independent datasets with AUC of 0.734. And the SHAP values indicated that preoperative urine leukocyte count was the most important variable to the prediction. Our study enables accurate predictions of infection in urology perioperative periods, the key variables can be interpreted better and more accurately to support clinical decision making.https://doi.org/10.1371/journal.pone.0317584 |
| spellingShingle | Weiqi Xia Miaomiao Zhang Xiaowei Zheng Zushuai Wu Zixue Xuan Ping Huang Xiuli Yang Machine learning for early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy. PLoS ONE |
| title | Machine learning for early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy. |
| title_full | Machine learning for early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy. |
| title_fullStr | Machine learning for early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy. |
| title_full_unstemmed | Machine learning for early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy. |
| title_short | Machine learning for early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy. |
| title_sort | machine learning for early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy |
| url | https://doi.org/10.1371/journal.pone.0317584 |
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