FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3096413426> ?p ?o ?g. }

• W3096413426 endingPage "196325" @default.
• W3096413426 startingPage "196299" @default.
• W3096413426 abstract "Between January and October of 2020, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has infected more than 34 million persons in a worldwide pandemic leading to over one million deaths worldwide (data from the Johns Hopkins University). Since the virus begun to spread, emergency departments were busy with COVID-19 patients for whom a quick decision regarding in- or outpatient care was required. The virus can cause characteristic abnormalities in chest radiographs (CXR), but, due to the low sensitivity of CXR, additional variables and criteria are needed to accurately predict risk. Here, we describe a computerized system primarily aimed at extracting the most relevant radiological, clinical, and laboratory variables for improving patient risk prediction, and secondarily at presenting an explainable machine learning system, which may provide simple decision criteria to be used by clinicians as a support for assessing patient risk. To achieve robust and reliable variable selection, Boruta and Random Forest (RF) are combined in a 10-fold cross-validation scheme to produce a variable importance estimate not biased by the presence of surrogates. The most important variables are then selected to train a RF classifier, whose rules may be extracted, simplified, and pruned to finally build an associative tree, particularly appealing for its simplicity. Results show that the radiological score automatically computed through a neural network is highly correlated with the score computed by radiologists, and that laboratory variables, together with the number of comorbidities, aid risk prediction. The prediction performance of our approach was compared to that that of generalized linear models and shown to be effective and robust. The proposed machine learning-based computational system can be easily deployed and used in emergency departments for rapid and accurate risk prediction in COVID-19 patients." @default.
• W3096413426 created "2020-11-09" @default.
• W3096413426 creator A5005837237 @default.
• W3096413426 creator A5023692886 @default.
• W3096413426 creator A5031580796 @default.
• W3096413426 creator A5034969250 @default.
• W3096413426 creator A5041624563 @default.
• W3096413426 creator A5047355764 @default.
• W3096413426 creator A5056607122 @default.
• W3096413426 creator A5071444502 @default.
• W3096413426 creator A5073533486 @default.
• W3096413426 creator A5075385368 @default.
• W3096413426 creator A5079008015 @default.
• W3096413426 creator A5079939753 @default.
• W3096413426 creator A5089196959 @default.
• W3096413426 date "2020-01-01" @default.
• W3096413426 modified "2023-10-17" @default.
• W3096413426 title "Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments" @default.
• W3096413426 cites W1504118426 @default.
• W3096413426 cites W1700449338 @default.
• W3096413426 cites W1787224781 @default.
• W3096413426 cites W1875061881 @default.
• W3096413426 cites W1982384746 @default.
• W3096413426 cites W1991323299 @default.
• W3096413426 cites W2012146899 @default.
• W3096413426 cites W2013022059 @default.
• W3096413426 cites W2015437563 @default.
• W3096413426 cites W2020960798 @default.
• W3096413426 cites W2021191744 @default.
• W3096413426 cites W2024395713 @default.
• W3096413426 cites W2032388524 @default.
• W3096413426 cites W2040323067 @default.
• W3096413426 cites W2042890970 @default.
• W3096413426 cites W2056079846 @default.
• W3096413426 cites W2058854776 @default.
• W3096413426 cites W2063291419 @default.
• W3096413426 cites W2064186732 @default.
• W3096413426 cites W2068358331 @default.
• W3096413426 cites W2072295766 @default.
• W3096413426 cites W2077461363 @default.
• W3096413426 cites W2078965693 @default.
• W3096413426 cites W2087023496 @default.
• W3096413426 cites W2096555119 @default.
• W3096413426 cites W2096863518 @default.
• W3096413426 cites W2102636708 @default.
• W3096413426 cites W2106824466 @default.
• W3096413426 cites W2107956883 @default.
• W3096413426 cites W2115098571 @default.
• W3096413426 cites W2115837368 @default.
• W3096413426 cites W2116781287 @default.
• W3096413426 cites W2121579674 @default.
• W3096413426 cites W2134843796 @default.
• W3096413426 cites W2143329262 @default.
• W3096413426 cites W2144328998 @default.
• W3096413426 cites W2156665896 @default.
• W3096413426 cites W2161375627 @default.
• W3096413426 cites W2165418121 @default.
• W3096413426 cites W2194775991 @default.
• W3096413426 cites W2238276407 @default.
• W3096413426 cites W2316816218 @default.
• W3096413426 cites W2424914361 @default.
• W3096413426 cites W2590150025 @default.
• W3096413426 cites W2620563544 @default.
• W3096413426 cites W2657631929 @default.
• W3096413426 cites W2745346577 @default.
• W3096413426 cites W2773594630 @default.
• W3096413426 cites W2801490189 @default.
• W3096413426 cites W2891381594 @default.
• W3096413426 cites W2899300491 @default.
• W3096413426 cites W2911964244 @default.
• W3096413426 cites W2945976633 @default.
• W3096413426 cites W2958682939 @default.
• W3096413426 cites W2958786037 @default.
• W3096413426 cites W2962858109 @default.
• W3096413426 cites W2963292722 @default.
• W3096413426 cites W2963420272 @default.
• W3096413426 cites W2963466845 @default.
• W3096413426 cites W2996589582 @default.
• W3096413426 cites W3001118548 @default.
• W3096413426 cites W3004825562 @default.
• W3096413426 cites W3007416325 @default.
• W3096413426 cites W3010511947 @default.
• W3096413426 cites W3010702679 @default.
• W3096413426 cites W3011149445 @default.
• W3096413426 cites W3011716991 @default.
• W3096413426 cites W3013130152 @default.
• W3096413426 cites W3013601031 @default.
• W3096413426 cites W3013957175 @default.
• W3096413426 cites W3014289208 @default.
• W3096413426 cites W3014524604 @default.
• W3096413426 cites W3015305373 @default.
• W3096413426 cites W3015698531 @default.
• W3096413426 cites W3017022286 @default.
• W3096413426 cites W3017116151 @default.
• W3096413426 cites W3017117984 @default.
• W3096413426 cites W3017246649 @default.
• W3096413426 cites W3017855299 @default.
• W3096413426 cites W3017891573 @default.

• next