Machine learning for the prediction of postoperative nosocomial pulmonary infection in patients with spinal cord injury

Li, Meng-Pan; Liu, Wencai; Wu, Jia-Bao; Luo, Kun; Liu, Y Y; Zhang, Yu; Xiao, Shining; Liu, Zhili; Huang, Shan-Hu; Liu, Jia-Ming

doi:10.1007/s00586-023-07772-8

Cited by 6 publications

(1 citation statement)

References 33 publications

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“…et al, 2021). Similarly, Li et al had designed a ML model to predict the incidence of pulmonary infections following spinal cord injuries (Li et al, 2023). With the advent of software for quality control and precise alignment of metagenomic sequencing data (such as kneaddata, bracken, Kracken, etc.)…”

Section: Introductionmentioning

confidence: 99%

Construction and validation of a machine learning model for the diagnosis of juvenile idiopathic arthritis based on fecal microbiota

Tu,

Liao,

Long

et al. 2024

Front. Cell. Infect. Microbiol.

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PurposeHuman gut microbiota has been shown to be significantly associated with various inflammatory diseases. Therefore, this study aimed to develop an excellent auxiliary tool for the diagnosis of juvenile idiopathic arthritis (JIA) based on fecal microbial biomarkers.MethodThe fecal metagenomic sequencing data associated with JIA were extracted from NCBI, and the sequencing data were transformed into the relative abundance of microorganisms by professional data cleaning (KneadData, Trimmomatic and Bowtie2) and comparison software (Kraken2 and Bracken). After that, the fecal microbes with high abundance were extracted for subsequent analysis. The extracted fecal microbes were further screened by least absolute shrinkage and selection operator (LASSO) regression, and the selected fecal microbe biomarkers were used for model training. In this study, we constructed six different machine learning (ML) models, and then selected the best model for constructing a JIA diagnostic tool by comparing the performance of the models based on a combined consideration of area under receiver operating characteristic curve (AUC), accuracy, specificity, F1 score, calibration curves and clinical decision curves. In addition, to further explain the model, Permutation Importance analysis and Shapley Additive Explanations (SHAP) were performed to understand the contribution of each biomarker in the prediction process.ResultA total of 231 individuals were included in this study, including 203 JIA patients and Non-JIA individuals. In the analysis of diversity at the genus level, the alpha diversity represented by Shannon value was not significantly different between the two groups, while the belt diversity was slightly different. After selection by LASSO regression, 10 fecal microbe biomarkers were selected for model training. By comparing six different models, the XGB model showed the best performance, which average AUC, accuracy and F1 score were 0.976, 0.914 and 0.952, respectively, thus being used to construct the final JIA diagnosis model.ConclusionA JIA diagnosis model based on XGB algorithm was constructed with excellent performance, which may assist physicians in early detection of JIA patients and improve the prognosis of JIA patients.

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Section: Introductionmentioning

confidence: 99%