FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4306194623> ?p ?o ?g. }

• W4306194623 endingPage "e0276116" @default.
• W4306194623 startingPage "e0276116" @default.
• W4306194623 abstract "Logistic regression (LR) is the most common prediction model in medicine. In recent years, supervised machine learning (ML) methods have gained popularity. However, there are many concerns about ML utility for small sample sizes. In this study, we aim to compare the performance of 7 algorithms in the prediction of 1-year mortality and clinical progression to AIDS in a small cohort of infants living with HIV from South Africa and Mozambique. The data set (n = 100) was randomly split into 70% training and 30% validation set. Seven algorithms (LR, Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Artificial Neural Network (ANN), and Elastic Net) were compared. The variables included as predictors were the same across the models including sociodemographic, virologic, immunologic, and maternal status features. For each of the models, a parameter tuning was performed to select the best-performing hyperparameters using 5 times repeated 10-fold cross-validation. A confusion-matrix was built to assess their accuracy, sensitivity, and specificity. RF ranked as the best algorithm in terms of accuracy (82,8%), sensitivity (78%), and AUC (0,73). Regarding specificity and sensitivity, RF showed better performance than the other algorithms in the external validation and the highest AUC. LR showed lower performance compared with RF, SVM, or KNN. The outcome of children living with perinatally acquired HIV can be predicted with considerable accuracy using ML algorithms. Better models would benefit less specialized staff in limited resources countries to improve prompt referral in case of high-risk clinical progression." @default.
• W4306194623 created "2022-10-14" @default.
• W4306194623 creator A5007441806 @default.
• W4306194623 creator A5012649381 @default.
• W4306194623 creator A5013013637 @default.
• W4306194623 creator A5014922741 @default.
• W4306194623 creator A5029916608 @default.
• W4306194623 creator A5035554662 @default.
• W4306194623 creator A5036924989 @default.
• W4306194623 creator A5039855616 @default.
• W4306194623 creator A5042369584 @default.
• W4306194623 creator A5042594419 @default.
• W4306194623 creator A5045836441 @default.
• W4306194623 creator A5047248287 @default.
• W4306194623 creator A5059175898 @default.
• W4306194623 creator A5059535736 @default.
• W4306194623 creator A5060300835 @default.
• W4306194623 creator A5065078265 @default.
• W4306194623 creator A5068165121 @default.
• W4306194623 creator A5070674047 @default.
• W4306194623 creator A5071315338 @default.
• W4306194623 creator A5071612716 @default.
• W4306194623 creator A5071639379 @default.
• W4306194623 creator A5081510932 @default.
• W4306194623 date "2022-10-14" @default.
• W4306194623 modified "2023-10-16" @default.
• W4306194623 title "Machine learning outperformed logistic regression classification even with limit sample size: A model to predict pediatric HIV mortality and clinical progression to AIDS" @default.
• W4306194623 cites W1602160603 @default.
• W4306194623 cites W1831050183 @default.
• W4306194623 cites W1999617260 @default.
• W4306194623 cites W2001804102 @default.
• W4306194623 cites W2006617902 @default.
• W4306194623 cites W2043376625 @default.
• W4306194623 cites W2097360283 @default.
• W4306194623 cites W2157963336 @default.
• W4306194623 cites W2496911238 @default.
• W4306194623 cites W2498119267 @default.
• W4306194623 cites W2525984666 @default.
• W4306194623 cites W2549666191 @default.
• W4306194623 cites W2737706773 @default.
• W4306194623 cites W2809006732 @default.
• W4306194623 cites W2861666812 @default.
• W4306194623 cites W2911964244 @default.
• W4306194623 cites W2918140740 @default.
• W4306194623 cites W2921462756 @default.
• W4306194623 cites W2953497237 @default.
• W4306194623 cites W2966671057 @default.
• W4306194623 cites W2969301077 @default.
• W4306194623 cites W2984856451 @default.
• W4306194623 cites W3006913750 @default.
• W4306194623 cites W4294541781 @default.
• W4306194623 cites W82055556 @default.
• W4306194623 doi "https://doi.org/10.1371/journal.pone.0276116" @default.
• W4306194623 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36240212" @default.
• W4306194623 hasPublicationYear "2022" @default.
• W4306194623 type Work @default.
• W4306194623 citedByCount "4" @default.
• W4306194623 countsByYear W43061946232023 @default.
• W4306194623 crossrefType "journal-article" @default.
• W4306194623 hasAuthorship W4306194623A5007441806 @default.
• W4306194623 hasAuthorship W4306194623A5012649381 @default.
• W4306194623 hasAuthorship W4306194623A5013013637 @default.
• W4306194623 hasAuthorship W4306194623A5014922741 @default.
• W4306194623 hasAuthorship W4306194623A5029916608 @default.
• W4306194623 hasAuthorship W4306194623A5035554662 @default.
• W4306194623 hasAuthorship W4306194623A5036924989 @default.
• W4306194623 hasAuthorship W4306194623A5039855616 @default.
• W4306194623 hasAuthorship W4306194623A5042369584 @default.
• W4306194623 hasAuthorship W4306194623A5042594419 @default.
• W4306194623 hasAuthorship W4306194623A5045836441 @default.
• W4306194623 hasAuthorship W4306194623A5047248287 @default.
• W4306194623 hasAuthorship W4306194623A5059175898 @default.
• W4306194623 hasAuthorship W4306194623A5059535736 @default.
• W4306194623 hasAuthorship W4306194623A5060300835 @default.
• W4306194623 hasAuthorship W4306194623A5065078265 @default.
• W4306194623 hasAuthorship W4306194623A5068165121 @default.
• W4306194623 hasAuthorship W4306194623A5070674047 @default.
• W4306194623 hasAuthorship W4306194623A5071315338 @default.
• W4306194623 hasAuthorship W4306194623A5071612716 @default.
• W4306194623 hasAuthorship W4306194623A5071639379 @default.
• W4306194623 hasAuthorship W4306194623A5081510932 @default.
• W4306194623 hasBestOaLocation W43061946231 @default.
• W4306194623 hasConcept C105795698 @default.
• W4306194623 hasConcept C119857082 @default.
• W4306194623 hasConcept C12267149 @default.
• W4306194623 hasConcept C129848803 @default.
• W4306194623 hasConcept C138602881 @default.
• W4306194623 hasConcept C151956035 @default.
• W4306194623 hasConcept C154945302 @default.
• W4306194623 hasConcept C169258074 @default.
• W4306194623 hasConcept C33923547 @default.
• W4306194623 hasConcept C41008148 @default.
• W4306194623 hasConcept C52001869 @default.
• W4306194623 hasConcept C71924100 @default.
• W4306194623 hasConcept C8642999 @default.
• W4306194623 hasConceptScore W4306194623C105795698 @default.
• W4306194623 hasConceptScore W4306194623C119857082 @default.
• W4306194623 hasConceptScore W4306194623C12267149 @default.

• next