Establishment of risk prediction model for postoperative liver injury after non-liver surgery based on different machine learning algorithms
Objective To construct a machine learning prediction model for postoperative liver injury in patients with non-liver surgery based on preoperative and intraoperative medication indicators. Methods A case-control study was conducted on 315 patients with liver injury after non-liver surgery selected f...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Army Medical University
2024-04-01
|
| Series: | 陆军军医大学学报 |
| Subjects: | |
| Online Access: | http://aammt.tmmu.edu.cn/html/202312018.htm |
| _version_ | 1827281095987560448 |
|---|---|
| author | SUN Yizhu LI Yujie LIANG Hao |
| author_facet | SUN Yizhu LI Yujie LIANG Hao |
| author_sort | SUN Yizhu |
| collection | DOAJ |
| description | Objective To construct a machine learning prediction model for postoperative liver injury in patients with non-liver surgery based on preoperative and intraoperative medication indicators. Methods A case-control study was conducted on 315 patients with liver injury after non-liver surgery selected from the databases developed by 3 large general hospitals from January 2014 to September 2022.With the positive/negative ratio of 1:3, 928 cases in corresponding period with non-liver surgery and without liver injury were randomly matched as negative control cases.These 1 243 patients were randomly divided into the modeling group (n=869) and the validation group (n=374) in a ratio of 7:3 using the R language setting code.Preoperative clinical indicators (basic information, medical history, relevant scale score, surgical information and results of laboratory tests) and intraoperative medication were used to construct the prediction model for liver injury after non-liver surgery based on 4 machine learning algorithms, k-nearest neighbor (KNN), support vector machine linear (SVM), logic regression (LR) and extreme gradient boosting (XGBoost).In the validation group, receiver operating characteristic (ROC) curve, precision-recall curve (P-R), decision curve analysis (DCA) curve, Kappa value, sensitivity, specificity, Brier score, and F1 score were applied to evaluate the efficacy of model. Results The model established by 4 machine learning algorithms to predict postoperative liver injury after non-liver surgery was optimal using the XGBoost algorithm.The area under the receiver operating characteristic curve (AUROC) was 0.916(95%CI: 0.883~0.949), area under the precision-recall curve (AUPRC) was 0.841, Brier score was 0.097, and sensitivity and specificity was 78.95% and 87.10%, respectively. Conclusion The postoperative liver injury prediction model for non-liver surgery based on the XGBoost algorithm has effective prediction for the occurrence of postoperative liver injury.
|
| first_indexed | 2024-04-24T08:53:29Z |
| format | Article |
| id | doaj.art-9d9d5d30a8ec453fa8425cc75476ab7c |
| institution | Directory Open Access Journal |
| issn | 2097-0927 |
| language | zho |
| last_indexed | 2024-04-24T08:53:29Z |
| publishDate | 2024-04-01 |
| publisher | Editorial Office of Journal of Army Medical University |
| record_format | Article |
| series | 陆军军医大学学报 |
| spelling | doaj.art-9d9d5d30a8ec453fa8425cc75476ab7c2024-04-16T09:14:26ZzhoEditorial Office of Journal of Army Medical University陆军军医大学学报2097-09272024-04-0146776076710.16016/j.2097-0927.202312018Establishment of risk prediction model for postoperative liver injury after non-liver surgery based on different machine learning algorithmsSUN Yizhu0 LI Yujie1 LIANG Hao2Department of Anesthesiology, First Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, ChinaDepartment of Anesthesiology, First Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, ChinaDepartment of Anesthesiology, First Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, ChinaObjective To construct a machine learning prediction model for postoperative liver injury in patients with non-liver surgery based on preoperative and intraoperative medication indicators. Methods A case-control study was conducted on 315 patients with liver injury after non-liver surgery selected from the databases developed by 3 large general hospitals from January 2014 to September 2022.With the positive/negative ratio of 1:3, 928 cases in corresponding period with non-liver surgery and without liver injury were randomly matched as negative control cases.These 1 243 patients were randomly divided into the modeling group (n=869) and the validation group (n=374) in a ratio of 7:3 using the R language setting code.Preoperative clinical indicators (basic information, medical history, relevant scale score, surgical information and results of laboratory tests) and intraoperative medication were used to construct the prediction model for liver injury after non-liver surgery based on 4 machine learning algorithms, k-nearest neighbor (KNN), support vector machine linear (SVM), logic regression (LR) and extreme gradient boosting (XGBoost).In the validation group, receiver operating characteristic (ROC) curve, precision-recall curve (P-R), decision curve analysis (DCA) curve, Kappa value, sensitivity, specificity, Brier score, and F1 score were applied to evaluate the efficacy of model. Results The model established by 4 machine learning algorithms to predict postoperative liver injury after non-liver surgery was optimal using the XGBoost algorithm.The area under the receiver operating characteristic curve (AUROC) was 0.916(95%CI: 0.883~0.949), area under the precision-recall curve (AUPRC) was 0.841, Brier score was 0.097, and sensitivity and specificity was 78.95% and 87.10%, respectively. Conclusion The postoperative liver injury prediction model for non-liver surgery based on the XGBoost algorithm has effective prediction for the occurrence of postoperative liver injury. http://aammt.tmmu.edu.cn/html/202312018.htmmachine learningpredicting modelpostoperative liver injury |
| spellingShingle | SUN Yizhu LI Yujie LIANG Hao Establishment of risk prediction model for postoperative liver injury after non-liver surgery based on different machine learning algorithms 陆军军医大学学报 machine learning predicting model postoperative liver injury |
| title | Establishment of risk prediction model for postoperative liver injury after non-liver surgery based on different machine learning algorithms |
| title_full | Establishment of risk prediction model for postoperative liver injury after non-liver surgery based on different machine learning algorithms |
| title_fullStr | Establishment of risk prediction model for postoperative liver injury after non-liver surgery based on different machine learning algorithms |
| title_full_unstemmed | Establishment of risk prediction model for postoperative liver injury after non-liver surgery based on different machine learning algorithms |
| title_short | Establishment of risk prediction model for postoperative liver injury after non-liver surgery based on different machine learning algorithms |
| title_sort | establishment of risk prediction model for postoperative liver injury after non liver surgery based on different machine learning algorithms |
| topic | machine learning predicting model postoperative liver injury |
| url | http://aammt.tmmu.edu.cn/html/202312018.htm |
| work_keys_str_mv | AT sunyizhu establishmentofriskpredictionmodelforpostoperativeliverinjuryafternonliversurgerybasedondifferentmachinelearningalgorithms AT liyujie establishmentofriskpredictionmodelforpostoperativeliverinjuryafternonliversurgerybasedondifferentmachinelearningalgorithms AT lianghao establishmentofriskpredictionmodelforpostoperativeliverinjuryafternonliversurgerybasedondifferentmachinelearningalgorithms |