Dataset-chemokines, cytokines, and biomarkers in the saliva of children with Sjögren's syndrome

Withanage, Miyuraj Harishchandra Hikkaduwa; Hernandez, Maria Paula Gomez; Starman, Emily E.; Davis, April; Zeng, Erliang; Brogden, Kim A.; Lanzel, Emily A.

doi:10.1016/j.dib.2021.107139

Cited by 7 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This process aimed to select feature subsets with the optimal performance and minimum size. 23 – 25 The input data were divided into 2 groups: pre-operative and combined (pre-operative and postoperative) variables. For each group, the algorithm started with an empty set of features and subsequently added variables.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning to Dynamically Predict In-Hospital Venous Thromboembolism After Inguinal Hernia Surgery: Results From the CHAT-1 Study

Yan

Ding

et al. 2023

Clin Appl Thromb Hemost

View full text Add to dashboard Cite

Background The accuracy of current prediction tools for venous thromboembolism (VTE) events following hernia surgery remains insufficient for individualized patient management strategies. To address this issue, we have developed a machine learning (ML)-based model to dynamically predict in-hospital VTE in Chinese patients after hernia surgery. Methods ML models for the prediction of postoperative VTE were trained on a cohort of 11 305 adult patients with hernia from the CHAT-1 trial, which included patients across 58 institutions in China. In data processing, data imputation was conducted using random forest (RF) algorithm, and balanced sampling was done by adaptive synthetic sampling algorithm. Data were split into a training cohort (80%) and internal validation cohort (20%) prior to oversampling. Clinical features available pre-operatively and postoperatively were separately selected using the Sequence Forward Selection algorithm. Nine-candidate ML models were applied to the pre-operative and combined datasets, and their performance was evaluated using various metrics, including area under the receiver operating characteristic curve (AUROC). Model interpretations were generated using importance scores, which were calculated by transforming model features into scaled variables and representing them in radar plots. Results The modeling cohort included 2856 patients, divided into 2536 cases for derivation and 320 cases for validation. Eleven pre-operative variables and 15 combined variables were explored as predictors related to in-hospital VTE. Acceptable-performing models for pre-operative data had an AUROC ≥ 0.60, including logistic regression, support vector machine with linear kernel (SVM_Linear), attentive interpretable Tabular learning (TabNet), and RF. For combined data, logistic regression, SVM_Linear, and TabNet had better performance, with an AUROC ≥ 0.65 for each model. Based on these models, 7 pre-operative predictors and 10 combined predictors were depicted in radar plots. Conclusions A ML-based approach for the identification of in-hospital VTE events after hernia surgery is feasible. TabNet showed acceptable performance, and might be useful to guide clinical decision making and VTE prevention. Further validated study will strengthen this finding.

show abstract

Section: Methodsmentioning

confidence: 99%

Machine Learning to Dynamically Predict In-Hospital Venous Thromboembolism After Inguinal Hernia Surgery: Results From the CHAT-1 Study

Yan

Ding

et al. 2023

Clin Appl Thromb Hemost

View full text Add to dashboard Cite

show abstract

“…Ranking was performed by Weka3 based on the predictive power of features with respective class labels. By aggregating all of the five feature rankings, we designed another feature ranking called Ensemble 35,36 . Furthermore, discrete sets of bacterial genera (union, AtLeast2, AtLeast3, AtLeast4, and SelectedByFive) were obtained using a Venn diagram representing overlapped features of the five feature selection methods aggregating ranks of all five forementioned feature selection methods 35 .…”

Section: Methodsmentioning

confidence: 99%

“…By aggregating all of the five feature rankings, we designed another feature ranking called Ensemble. 35,36 Furthermore, discrete sets of bacterial genera (union, AtLeast2, AtLeast3, AtLeast4, and SelectedByFive) were obtained using a Venn diagram representing overlapped features of the five feature selection methods aggregating ranks of all five forementioned feature selection methods. 35 Briefly, the Union feature set at a given rank k is the union of all top k features selected by five methods.…”

Section: Feature Selectionmentioning

confidence: 99%

“…35,36 Furthermore, discrete sets of bacterial genera (union, AtLeast2, AtLeast3, AtLeast4, and SelectedByFive) were obtained using a Venn diagram representing overlapped features of the five feature selection methods aggregating ranks of all five forementioned feature selection methods. 35 Briefly, the Union feature set at a given rank k is the union of all top k features selected by five methods. The SelectedByFive feature set at a given rank k are the common features of all top k feature sets each selected by five methods.…”

Section: Feature Selectionmentioning

confidence: 99%

“…After that, we created five feature aggregation methods called union, AtLeast2, AtLeast3, AtLeast4, and SelectedByFive as previously described. 35,36 These methods created discrete candidate feature clusters for classifier-based evaluation. Finally, we used Scikit learn package's 28 sklearn.ensemble.…”

Section: Databasementioning

confidence: 99%

See 2 more Smart Citations

COVID‐19 associated oral and oropharyngeal microbiome: Systematic review and meta‐analysis

Ganesan,

Peter,

Withanage

et al. 2023

Periodontology 2000

Self Cite

View full text Add to dashboard Cite

Three years into the coronavirus disease 2019 (COVID‐19) pandemic, there are still growing concerns with the emergence of different variants, unknown long‐ and short‐term effects of the virus, and potential biological mechanisms underlying etiopathogenesis and increased risk for morbidity and mortality. The role of the microbiome in human physiology and the initiation and progression of several oral and systemic diseases have been actively studied in the past decade. With the proof of viral transmission, carriage, and a potential role in etiopathogenesis, saliva and the oral environment have been a focus of COVID‐19 research beyond diagnostic purposes. The oral environment hosts diverse microbial communities and contributes to human oral and systemic health. Several investigations have identified disruptions in the oral microbiome in COVID‐19 patients. However, all these studies are cross‐sectional in nature and present heterogeneity in study design, techniques, and analysis. Therefore, in this undertaking, we (a) systematically reviewed the current literature associating COVID‐19 with changes in the microbiome; (b) performed a re‐analysis of publicly available data as a means to standardize the analysis, and (c) reported alterations in the microbial characteristics in COVID‐19 patients compared to negative controls. Overall, we identified that COVID‐19 is associated with oral microbial dysbiosis with significant reduction in diversity. However, alterations in specific bacterial members differed across the study. Re‐analysis from our pipeline shed light on Neisseria as the potential key microbial member associated with COVID‐19.

show abstract

The emerging paradigm in pediatric rheumatology: harnessing the power of artificial intelligence

Koker,

Sahin,

Yildiz

et al. 2024

Rheumatol Int

View full text Add to dashboard Cite

Artificial intelligence algorithms, with roots extending into the past but experiencing a resurgence and evolution in recent years due to their superiority over traditional methods and contributions to human capabilities, have begun to make their presence felt in the field of pediatric rheumatology. In the ever-evolving realm of pediatric rheumatology, there have been incremental advancements supported by artificial intelligence in understanding and stratifying diseases, developing biomarkers, refining visual analyses, and facilitating individualized treatment approaches. However, like in many other domains, these strides have yet to gain clinical applicability and validation, and ethical issues remain unresolved. Furthermore, mastering different and novel terminologies appears challenging for clinicians. This review aims to provide a comprehensive overview of the current literature, categorizing algorithms and their applications, thus offering a fresh perspective on the nascent relationship between pediatric rheumatology and artificial intelligence, highlighting both its advancements and constraints.

show abstract

Dataset-chemokines, cytokines, and biomarkers in the saliva of children with Sjögren's syndrome

Cited by 7 publications

References 7 publications

Machine Learning to Dynamically Predict In-Hospital Venous Thromboembolism After Inguinal Hernia Surgery: Results From the CHAT-1 Study

Machine Learning to Dynamically Predict In-Hospital Venous Thromboembolism After Inguinal Hernia Surgery: Results From the CHAT-1 Study

COVID‐19 associated oral and oropharyngeal microbiome: Systematic review and meta‐analysis

The emerging paradigm in pediatric rheumatology: harnessing the power of artificial intelligence

Contact Info

Product

Resources

About