In:
Frontiers in Neuroinformatics, Frontiers Media SA, Vol. 16 ( 2023-1-13)
Abstract:
To understand the infection characteristics and risk factors for infection by analyzing multicenter clinical data of newly diagnosed multiple myeloma (NDMM) patients. Methods This study reviewed 564 NDMM patients from 2 large tertiary hospitals from January 2018 to December 2021, of whom 395 comprised the training set and 169 comprised the validation set. Thirty-eight variables from first admission records were collected, including patient demographic characteristics, clinical scores and characteristics, laboratory indicators, complications, and medication history, and key variables were screened using the Lasso method. Multiple machine learning algorithms were compared, and the best performing algorithm was used to build a machine learning prediction model. The model performance was evaluated using the AUC, accuracy, and Youden’s index. Finally, the SHAP package was used to assess two cases and demonstrate the application of the model. Results In this study, 15 important key variables were selected, namely, age, ECOG, osteolytic disruption, VCD, neutrophils, lymphocytes, monocytes, hemoglobin, platelets, albumin, creatinine, lactate dehydrogenase, affected globulin, β2 microglobulin, and preventive medicine. The predictive performance of the XGBoost model was significantly better than that of the other models (AUROC: 0.8664), and it also performed well for the expected dataset (accuracy: 68.64%). Conclusion A machine learning algorithm was used to establish an infection prediction model for NDMM patients that was simple, convenient, validated, and performed well in reducing the incidence of infection and improving the prognosis of patients.
Type of Medium:
Online Resource
ISSN:
1662-5196
DOI:
10.3389/fninf.2022.1063610
DOI:
10.3389/fninf.2022.1063610.s001
DOI:
10.3389/fninf.2022.1063610.s002
DOI:
10.3389/fninf.2022.1063610.s003
DOI:
10.3389/fninf.2022.1063610.s004
Language:
Unknown
Publisher:
Frontiers Media SA
Publication Date:
2023
detail.hit.zdb_id:
2452979-5
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