FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4242992183> ?p ?o ?g. }

• W4242992183 abstract "<sec> <title>BACKGROUND</title> Delirium is a temporary mental disorder that occasionally affects patients undergoing surgery, especially cardiac surgery. It is strongly associated with major adverse events, which in turn leads to increased cost and poor outcomes (eg, need for nursing home due to cognitive impairment, stroke, and death). The ability to foresee patients at risk of delirium will guide the timely initiation of multimodal preventive interventions, which will aid in reducing the burden and negative consequences associated with delirium. Several studies have focused on the prediction of delirium. However, the number of studies in cardiac surgical patients that have used machine learning methods is very limited. </sec> <sec> <title>OBJECTIVE</title> This study aimed to explore the application of several machine learning predictive models that can pre-emptively predict delirium in patients undergoing cardiac surgery and compare their performance. </sec> <sec> <title>METHODS</title> We investigated a number of machine learning methods to develop models that can predict delirium after cardiac surgery. A clinical dataset comprising over 5000 actual patients who underwent cardiac surgery in a single center was used to develop the models using logistic regression, artificial neural networks (ANN), support vector machines (SVM), Bayesian belief networks (BBN), naïve Bayesian, random forest, and decision trees. </sec> <sec> <title>RESULTS</title> Only 507 out of 5584 patients (11.4%) developed delirium. We addressed the underlying class imbalance, using random undersampling, in the training dataset. The final prediction performance was validated on a separate test dataset. Owing to the target class imbalance, several measures were used to evaluate algorithm’s performance for the delirium class on the test dataset. Out of the selected algorithms, the SVM algorithm had the best F1 score for positive cases, kappa, and positive predictive value (40.2%, 29.3%, and 29.7%, respectively) with a <italic>P</italic>=.01, .03, .02, respectively. The ANN had the best receiver-operator area-under the curve (78.2%; <italic>P</italic>=.03). The BBN had the best precision-recall area-under the curve for detecting positive cases (30.4%; <italic>P</italic>=.03). </sec> <sec> <title>CONCLUSIONS</title> Although delirium is inherently complex, preventive measures to mitigate its negative effect can be applied proactively if patients at risk are prospectively identified. Our results highlight 2 important points: (1) addressing class imbalance on the training dataset will augment machine learning model’s performance in identifying patients likely to develop postoperative delirium, and (2) as the prediction of postoperative delirium is difficult because it is multifactorial and has complex pathophysiology, applying machine learning methods (complex or simple) may improve the prediction by revealing hidden patterns, which will lead to cost reduction by prevention of complications and will optimize patients’ outcomes. </sec>" @default.
• W4242992183 created "2022-05-12" @default.
• W4242992183 creator A5002864681 @default.
• W4242992183 creator A5017186555 @default.
• W4242992183 creator A5023659508 @default.
• W4242992183 creator A5058424348 @default.
• W4242992183 date "2019-06-11" @default.
• W4242992183 modified "2023-09-27" @default.
• W4242992183 title "Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study (Preprint)" @default.
• W4242992183 cites W1794427698 @default.
• W4242992183 cites W1966716734 @default.
• W4242992183 cites W1968670396 @default.
• W4242992183 cites W1990302430 @default.
• W4242992183 cites W1996448539 @default.
• W4242992183 cites W2028246833 @default.
• W4242992183 cites W2030739289 @default.
• W4242992183 cites W2047638046 @default.
• W4242992183 cites W2077975418 @default.
• W4242992183 cites W2089926819 @default.
• W4242992183 cites W2090077439 @default.
• W4242992183 cites W2106997285 @default.
• W4242992183 cites W2116601790 @default.
• W4242992183 cites W2119518806 @default.
• W4242992183 cites W2121044961 @default.
• W4242992183 cites W2138928595 @default.
• W4242992183 cites W2144249679 @default.
• W4242992183 cites W2145188918 @default.
• W4242992183 cites W2590921856 @default.
• W4242992183 cites W2622380070 @default.
• W4242992183 cites W2657631929 @default.
• W4242992183 cites W2754009522 @default.
• W4242992183 cites W2760281097 @default.
• W4242992183 cites W2772577987 @default.
• W4242992183 cites W2774161943 @default.
• W4242992183 cites W2964315079 @default.
• W4242992183 doi "https://doi.org/10.2196/preprints.14993" @default.
• W4242992183 hasPublicationYear "2019" @default.
• W4242992183 type Work @default.
• W4242992183 citedByCount "0" @default.
• W4242992183 crossrefType "posted-content" @default.
• W4242992183 hasAuthorship W4242992183A5002864681 @default.
• W4242992183 hasAuthorship W4242992183A5017186555 @default.
• W4242992183 hasAuthorship W4242992183A5023659508 @default.
• W4242992183 hasAuthorship W4242992183A5058424348 @default.
• W4242992183 hasBestOaLocation W42429921832 @default.
• W4242992183 hasConcept C118552586 @default.
• W4242992183 hasConcept C119857082 @default.
• W4242992183 hasConcept C12267149 @default.
• W4242992183 hasConcept C126322002 @default.
• W4242992183 hasConcept C151956035 @default.
• W4242992183 hasConcept C154945302 @default.
• W4242992183 hasConcept C169258074 @default.
• W4242992183 hasConcept C177713679 @default.
• W4242992183 hasConcept C27415008 @default.
• W4242992183 hasConcept C2778789114 @default.
• W4242992183 hasConcept C2779753318 @default.
• W4242992183 hasConcept C33724603 @default.
• W4242992183 hasConcept C41008148 @default.
• W4242992183 hasConcept C52001869 @default.
• W4242992183 hasConcept C71924100 @default.
• W4242992183 hasConceptScore W4242992183C118552586 @default.
• W4242992183 hasConceptScore W4242992183C119857082 @default.
• W4242992183 hasConceptScore W4242992183C12267149 @default.
• W4242992183 hasConceptScore W4242992183C126322002 @default.
• W4242992183 hasConceptScore W4242992183C151956035 @default.
• W4242992183 hasConceptScore W4242992183C154945302 @default.
• W4242992183 hasConceptScore W4242992183C169258074 @default.
• W4242992183 hasConceptScore W4242992183C177713679 @default.
• W4242992183 hasConceptScore W4242992183C27415008 @default.
• W4242992183 hasConceptScore W4242992183C2778789114 @default.
• W4242992183 hasConceptScore W4242992183C2779753318 @default.
• W4242992183 hasConceptScore W4242992183C33724603 @default.
• W4242992183 hasConceptScore W4242992183C41008148 @default.
• W4242992183 hasConceptScore W4242992183C52001869 @default.
• W4242992183 hasConceptScore W4242992183C71924100 @default.
• W4242992183 hasLocation W42429921831 @default.
• W4242992183 hasLocation W42429921832 @default.
• W4242992183 hasOpenAccess W4242992183 @default.
• W4242992183 hasPrimaryLocation W42429921831 @default.
• W4242992183 hasRelatedWork W2979979539 @default.
• W4242992183 hasRelatedWork W3127425528 @default.
• W4242992183 hasRelatedWork W3137532542 @default.
• W4242992183 hasRelatedWork W4205958290 @default.
• W4242992183 hasRelatedWork W4225312515 @default.
• W4242992183 hasRelatedWork W4246246790 @default.
• W4242992183 hasRelatedWork W4281846282 @default.
• W4242992183 hasRelatedWork W4310982196 @default.
• W4242992183 hasRelatedWork W4311106074 @default.
• W4242992183 hasRelatedWork W4376059206 @default.
• W4242992183 isParatext "false" @default.
• W4242992183 isRetracted "false" @default.
• W4242992183 workType "article" @default.