FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4319837072> ?p ?o ?g. }

• W4319837072 abstract "<sec> <title>BACKGROUND</title> Cardiac arrest (CA) is a global public health challenge. Accurate prediction of outcomes is a critical aspect of the management of patients after CA. This study will develop and validate an applicable machine learning (ML) model to predict in-hospital mortality of CA in intensive care units. This study will develop and validate an machine learning (ML) model to predict in-hospital mortality of CA in ICU. </sec> <sec> <title>OBJECTIVE</title> The study aims to develop and validate machine learning models to predict in-hospital mortality of CA in ICU. </sec> <sec> <title>METHODS</title> Patients with CA were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database, and further divided into the training set (80%) and validation set (20%). The primary outcome was in-hospital mortality. The best model was selected from 11 ML algorithms and 3 time periods of 24 hours, 48 hours, and 72 hours. SHapley Additive exPlanations (SHAP) was applied to visualize the importance of features, while recursive feature elimination (RFE) was performed to figure out key features. The optimal compact model was developed based on selected key features, and its performance was proven based on the validation set. In addition, a Web-based tool was designed to applied this model to clinical practice. </sec> <sec> <title>RESULTS</title> 721 patients were included in this study, dividing into the training set (80%, n=576) and the validation set (20%, n=145). The 72-hour CatBoost model obtained the highest area under the receiver operating characteristic (AUROC) of 0.839. Thirteen variables were ultimately selected as key features, and the importance of each feature was visualized by SHAP. The compact model achieved the greatest AUROC of 0.862 in validation, which better than other models and SOFA (AUROC: 0.650). The Web-based tool was convenient for clinicians to use our model. </sec> <sec> <title>CONCLUSIONS</title> CatBoost model had great prediction performance of in-hospital mortality in CA patients. </sec>" @default.
• W4319837072 created "2023-02-11" @default.
• W4319837072 creator A5010295724 @default.
• W4319837072 creator A5015055246 @default.
• W4319837072 creator A5027953387 @default.
• W4319837072 creator A5031105985 @default.
• W4319837072 creator A5047855118 @default.
• W4319837072 creator A5049464184 @default.
• W4319837072 creator A5052620727 @default.
• W4319837072 creator A5061523965 @default.
• W4319837072 creator A5067764982 @default.
• W4319837072 creator A5068428294 @default.
• W4319837072 date "2023-01-15" @default.
• W4319837072 modified "2023-10-18" @default.
• W4319837072 title "Machine learning-based prediction for outcomes of cardiac arrest in intensive care units: Model Development and Validation Study (Preprint)" @default.
• W4319837072 cites W1919216911 @default.
• W4319837072 cites W2003098385 @default.
• W4319837072 cites W2021207198 @default.
• W4319837072 cites W2060097892 @default.
• W4319837072 cites W2107329825 @default.
• W4319837072 cites W2181981146 @default.
• W4319837072 cites W2396881363 @default.
• W4319837072 cites W2546680543 @default.
• W4319837072 cites W2790308961 @default.
• W4319837072 cites W2800318229 @default.
• W4319837072 cites W2896314083 @default.
• W4319837072 cites W2899067514 @default.
• W4319837072 cites W3007582628 @default.
• W4319837072 cites W3093949537 @default.
• W4319837072 cites W3094948551 @default.
• W4319837072 cites W3106725557 @default.
• W4319837072 cites W3106757763 @default.
• W4319837072 cites W3130832301 @default.
• W4319837072 cites W3200436355 @default.
• W4319837072 cites W3209982237 @default.
• W4319837072 cites W3210038387 @default.
• W4319837072 cites W3214829709 @default.
• W4319837072 cites W3215470728 @default.
• W4319837072 cites W4206163452 @default.
• W4319837072 cites W4206920898 @default.
• W4319837072 cites W4214764980 @default.
• W4319837072 cites W4224885775 @default.
• W4319837072 cites W4225847424 @default.
• W4319837072 cites W4229025171 @default.
• W4319837072 cites W4285588130 @default.
• W4319837072 doi "https://doi.org/10.2196/preprints.45741" @default.
• W4319837072 hasPublicationYear "2023" @default.
• W4319837072 type Work @default.
• W4319837072 citedByCount "0" @default.
• W4319837072 crossrefType "posted-content" @default.
• W4319837072 hasAuthorship W4319837072A5010295724 @default.
• W4319837072 hasAuthorship W4319837072A5015055246 @default.
• W4319837072 hasAuthorship W4319837072A5027953387 @default.
• W4319837072 hasAuthorship W4319837072A5031105985 @default.
• W4319837072 hasAuthorship W4319837072A5047855118 @default.
• W4319837072 hasAuthorship W4319837072A5049464184 @default.
• W4319837072 hasAuthorship W4319837072A5052620727 @default.
• W4319837072 hasAuthorship W4319837072A5061523965 @default.
• W4319837072 hasAuthorship W4319837072A5067764982 @default.
• W4319837072 hasAuthorship W4319837072A5068428294 @default.
• W4319837072 hasConcept C108583219 @default.
• W4319837072 hasConcept C119857082 @default.
• W4319837072 hasConcept C136764020 @default.
• W4319837072 hasConcept C138885662 @default.
• W4319837072 hasConcept C154945302 @default.
• W4319837072 hasConcept C177264268 @default.
• W4319837072 hasConcept C177713679 @default.
• W4319837072 hasConcept C199360897 @default.
• W4319837072 hasConcept C26517878 @default.
• W4319837072 hasConcept C2776401178 @default.
• W4319837072 hasConcept C2778827112 @default.
• W4319837072 hasConcept C2987404301 @default.
• W4319837072 hasConcept C38652104 @default.
• W4319837072 hasConcept C41008148 @default.
• W4319837072 hasConcept C41895202 @default.
• W4319837072 hasConcept C43169469 @default.
• W4319837072 hasConcept C58471807 @default.
• W4319837072 hasConcept C71924100 @default.
• W4319837072 hasConceptScore W4319837072C108583219 @default.
• W4319837072 hasConceptScore W4319837072C119857082 @default.
• W4319837072 hasConceptScore W4319837072C136764020 @default.
• W4319837072 hasConceptScore W4319837072C138885662 @default.
• W4319837072 hasConceptScore W4319837072C154945302 @default.
• W4319837072 hasConceptScore W4319837072C177264268 @default.
• W4319837072 hasConceptScore W4319837072C177713679 @default.
• W4319837072 hasConceptScore W4319837072C199360897 @default.
• W4319837072 hasConceptScore W4319837072C26517878 @default.
• W4319837072 hasConceptScore W4319837072C2776401178 @default.
• W4319837072 hasConceptScore W4319837072C2778827112 @default.
• W4319837072 hasConceptScore W4319837072C2987404301 @default.
• W4319837072 hasConceptScore W4319837072C38652104 @default.
• W4319837072 hasConceptScore W4319837072C41008148 @default.
• W4319837072 hasConceptScore W4319837072C41895202 @default.
• W4319837072 hasConceptScore W4319837072C43169469 @default.
• W4319837072 hasConceptScore W4319837072C58471807 @default.
• W4319837072 hasConceptScore W4319837072C71924100 @default.
• W4319837072 hasLocation W43198370721 @default.
• W4319837072 hasOpenAccess W4319837072 @default.
• W4319837072 hasPrimaryLocation W43198370721 @default.
• W4319837072 hasRelatedWork W2963749793 @default.

• next