Machine learning-based models for predicting mortality and acute kidney injury in critical pulmonary embolism

Wang, Geng; Xu, Jiatang; Lin, Xiao; Lai, Weijie; Lv, Lin; Peng, Senyi; Li, Kechen; Luo, Mingli; Chen, Jiale; Zhu, Dongxi; Chen, Xiong; Yao, Chen; Wu, Shaoxu

doi:10.1186/s12872-023-03363-z

Cited by 5 publications

(2 citation statements)

References 39 publications

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“…Su et al devised a ML technique to discern the severity of PE using clinical features and hematological indicators ( 12 ). Wang et al utilized ML to forecast the 30-day mortality rate of critically ill PE patients ( 13 ). These studies underscore the promising application of ML techniques, developed from high-dimensional medical data, for PE.…”

Section: Introductionmentioning

confidence: 99%

Construction and validation of risk prediction models for pulmonary embolism in hospitalized patients based on different machine learning methods

Huang,

Peng

et al. 2024

Front. Cardiovasc. Med.

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ObjectiveThis study aims to apply different machine learning (ML) methods to construct risk prediction models for pulmonary embolism (PE) in hospitalized patients, and to evaluate and compare the predictive efficacy and clinical benefit of each model.MethodsWe conducted a retrospective study involving 332 participants (172 PE positive cases and 160 PE negative cases) recruited from Guangdong Medical University. Participants were randomly divided into a training group (70%) and a validation group (30%). Baseline data were analyzed using univariate analysis, and potential independent risk factors associated with PE were further identified through univariate and multivariate logistic regression analysis. Six ML models, namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and AdaBoost were developed. The predictive efficacy of each model was compared using the receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC). Clinical benefit was assessed using decision curve analysis (DCA).ResultsLogistic regression analysis identified lower extremity deep venous thrombosis, elevated D-dimer, shortened activated partial prothrombin time, and increased red blood cell distribution width as potential independent risk factors for PE. Among the six ML models, the RF model achieved the highest AUC of 0.778. Additionally, DCA consistently indicated that the RF model offered the greatest clinical benefit.ConclusionThis study developed six ML models, with the RF model exhibiting the highest predictive efficacy and clinical benefit in the identification and prediction of PE occurrence in hospitalized patients.

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Section: Introductionmentioning

confidence: 99%

Construction and validation of risk prediction models for pulmonary embolism in hospitalized patients based on different machine learning methods

Huang,

Peng

et al. 2024

Front. Cardiovasc. Med.

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“…Their release facilitates neutrophil migration into adjacent tissues via transcellular and paracellular routes, overcoming the endothelial barrier. One important first step in exacerbating local thrombo-inflammatory damage responses that are aggravated in acute PE is neutrophil extravasation [39].…”

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confidence: 99%

Predicting biomarker for the acute pulmonary embolism by using gene ontology and machine learning

Zhou,

Duan,

Chen

et al. 2023

Preprint

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Key PointsEarly and Accurate Diagnosis Essential:Acute pulmonary embolism (PE) is a critical condition that demands prompt and precise diagnosis for effective treatment.Limitations of Current Diagnostics:Existing diagnostic methods like Computed Tomography Pulmonary Angiography (CTPA) have certain limitations, leading to the exploration of alternative approaches.Potential of Blood-Based Biomarkers:A recent study focused on identifying blood-based biomarkers for PE. This involved using gene ontology analysis and machine learning methods to analyze gene expression data from both PE patients and healthy controls.Gene Selection and Analysis:The study selected 20 genes for detailed analysis. These included various coagulation factors, fibrinolytic genes, and inflammation markers. Gene Ontology enrichment analysis was performed to understand the biological processes and molecular functions of these genes.Machine Learning for Diagnosis:Supervised machine learning algorithms were utilized to create classification models using the expression levels of these 20 genes. The models demonstrated promising results in distinguishing PE patients from healthy individuals.Acute pulmonary embolism (PE) is a life-threatening condition requiring early and accurate diagnosis. Current diagnostic methods like CTPA have limitations, and a study aimed to identify potential blood-based biomarkers for PE using gene ontology analysis and machine learning methods. Gene expression data of PE patients and healthy controls were obtained from the Gene Expression Omnibus database. A total of 20 genes were selected for further analysis, including coagulation factors F7, F10, F12, fibrinolytic genes PLAT, SERPINE1 and SERPINE2, and inflammation markers SELE, VCAM1 and ICAM. Gene Ontology enrichment analysis was performed to identify biological processes and molecular functions overrepresented among the candidate genes. Supervised machine learning algorithms were applied to build classification models using the expression levels of the 20 genes as features. Nested cross-validation was employed to assess model performance. The RF model achieved the highest area under the receiver operating characteristic curve of 0.89, indicating excellent discrimination between PE patients and controls based on the gene expression signature. Validation in larger cohorts is warranted to clinically translate these findings into a non-invasive diagnostic test for PE.

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Integration of artificial intelligence in clinical laboratory medicine: Advancements and challenges

Xie,

Jia,

Liu

2024

Interdisciplinary Medicine

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Artificial intelligence (AI)‐driven analysis of comprehensive clinical parameters is bringing about a significant transformation in traditional routine clinical laboratory testing. This transformation impacts the prediction, prevention, diagnosis, and prognosis of human diseases. AI possesses the capability to efficiently analyze and process vast and intricate datasets, thereby facilitating the development of diverse and efficient diagnostic or predictive models. This advancement is fueling significant improvements in laboratory quality, automation, and the accuracy of diagnoses. In this context, we conducted a thorough review and discussion on the progression of AI applications in clinical laboratory medicine, encompassing advancements, implementation, and challenges. Our conclusion underscores that integrating AI into clinical laboratory testing will notably propel personalized precision medicine forward and enhance diagnostic accuracy, especially benefiting patients for whom accurate diagnoses are elusive through traditional laboratory testing systems.

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Machine learning-based models for predicting mortality and acute kidney injury in critical pulmonary embolism

Cited by 5 publications

References 39 publications

Construction and validation of risk prediction models for pulmonary embolism in hospitalized patients based on different machine learning methods

Construction and validation of risk prediction models for pulmonary embolism in hospitalized patients based on different machine learning methods

Predicting biomarker for the acute pulmonary embolism by using gene ontology and machine learning

Integration of artificial intelligence in clinical laboratory medicine: Advancements and challenges

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