SVM-Based Model Combining Patients’ Reported Outcomes and Lymphocyte Phenotypes of Depression in Systemic Lupus Erythematosus

Dong, Chen; Yang, Nengjie; Zhao, Rui; Yang, Ying; Gu, Xianfeng; Fu, Ting; Sun, Chi; Gu, Zhifeng

doi:10.3390/biom13050723

Cited by 3 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…NB is rooted in Bayes’ theorem and performs classification by calculating the posterior probabilities of different categories under given feature conditions [ 31 ]. SVM is a supervised learning algorithm that makes predictions by identifying the optimal separating hyperplane [ 32 ]. LR is a linear model that predicts probabilities based on the logistic function [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

Personalized prediction of mortality in patients with acute ischemic stroke using explainable artificial intelligence

Xu,

Li,

Zhang

et al. 2024

Eur J Med Res

View full text Add to dashboard Cite

Background Research into the acute kidney disease (AKD) after acute ischemic stroke (AIS) is rare, and how clinical features influence its prognosis remain unknown. We aim to employ interpretable machine learning (ML) models to study AIS and clarify its decision-making process in identifying the risk of mortality. Methods We conducted a retrospective cohort study involving AIS patients from January 2020 to June 2021. Patient data were randomly divided into training and test sets. Eight ML algorithms were employed to construct predictive models for mortality. The performance of the best model was evaluated using various metrics. Furthermore, we created an artificial intelligence (AI)-driven web application that leveraged the top ten most crucial features for mortality prediction. Results The study cohort consisted of 1633 AIS patients, among whom 257 (15.74%) developed subacute AKD, 173 (10.59%) experienced AKI recovery, and 65 (3.98%) met criteria for both AKI and AKD. The mortality rate stood at 4.84%. The LightGBM model displayed superior performance, boasting an AUROC of 0.96 for mortality prediction. The top five features linked to mortality were ACEI/ARE, renal function trajectories, neutrophil count, diuretics, and serum creatinine. Moreover, we designed a web application using the LightGBM model to estimate mortality risk. Conclusions Complete renal function trajectories, including AKI and AKD, are vital for fitting mortality in AIS patients. An interpretable ML model effectively clarified its decision-making process for identifying AIS patients at risk of mortality. The AI-driven web application has the potential to contribute to the development of personalized early mortality prevention.

show abstract

Section: Methodsmentioning

confidence: 99%

Personalized prediction of mortality in patients with acute ischemic stroke using explainable artificial intelligence

Xu,

Li,

Zhang

et al. 2024

Eur J Med Res

View full text Add to dashboard Cite

show abstract

“… 117 For NPSLE, different T and B cell subsets predicted depression in patients with SLE. 127 128 Proteomics using cerebrospinal fluid demonstrated that CST6, L-selectin, Trappin-2, KLK5 and TCN2 could distinguish NPSLE from SLE controls (non-NPSLE). 124 Other reports using single-cell RNA sequencing data compared biomarkers for NPSLE to multiple sclerosis 103 and vascular dementia.…”

Section: Key Sle Findings By ML Reportsmentioning

confidence: 99%

Systemic lupus in the era of machine learning medicine

Zhan,

Buhler,

Chen

et al. 2024

Lupus Sci Med

View full text Add to dashboard Cite

Artificial intelligence and machine learning applications are emerging as transformative technologies in medicine. With greater access to a diverse range of big datasets, researchers are turning to these powerful techniques for data analysis. Machine learning can reveal patterns and interactions between variables in large and complex datasets more accurately and efficiently than traditional statistical methods. Machine learning approaches open new possibilities for studying SLE, a multifactorial, highly heterogeneous and complex disease. Here, we discuss how machine learning methods are rapidly being integrated into the field of SLE research. Recent reports have focused on building prediction models and/or identifying novel biomarkers using both supervised and unsupervised techniques for understanding disease pathogenesis, early diagnosis and prognosis of disease. In this review, we will provide an overview of machine learning techniques to discuss current gaps, challenges and opportunities for SLE studies. External validation of most prediction models is still needed before clinical adoption. Utilisation of deep learning models, access to alternative sources of health data and increased awareness of the ethics, governance and regulations surrounding the use of artificial intelligence in medicine will help propel this exciting field forward.

show abstract

Study on risk factor analysis and model prediction of hyperuricemia in different populations

Hou,

Shi,

et al. 2024

Front. Nutr.

View full text Add to dashboard Cite

ObjectivesThe purpose of the present study was to explore the influencing factors of hyperuricemia (HUA) in different populations in Shandong Province based on clinical biochemical indicators. A prediction model for HUA was constructed to aid in the early prevention and screening of HUA.MethodsIn total, 705 cases were collected from five hospitals, and the risk factors were analyzed by Pearson correlation analysis, binary logistic regression, and receiver operating characteristic (ROC) curve in the gender and age groups. All data were divided into a training set and test set (7:3). The training set included age, gender, total protein (TP), low-density lipoprotein cholesterol (LDL-C), and 15 other indicators. The random forest (RF) and support vector machine (SVM) methods were used to build the HUA model, and model performances were evaluated through 10-fold cross-validation to select the optimal method. Finally, features were extracted, and the ROC curve of the test set was generated.ResultsTP, LDL-C, and glucose (GLU) were risk factors for HUA, and the area under the curve (AUC) value of the SVM validation set was 0.875.ConclusionThe SVM model based on clinical biochemical indicators has good predictive ability for HUA, thus providing a reference for the diagnosis of HUA and the development of an HUA prediction model.

show abstract

SVM-Based Model Combining Patients’ Reported Outcomes and Lymphocyte Phenotypes of Depression in Systemic Lupus Erythematosus

Cited by 3 publications

References 34 publications

Personalized prediction of mortality in patients with acute ischemic stroke using explainable artificial intelligence

Personalized prediction of mortality in patients with acute ischemic stroke using explainable artificial intelligence

Systemic lupus in the era of machine learning medicine

Study on risk factor analysis and model prediction of hyperuricemia in different populations

Contact Info

Product

Resources

About