FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3147572009> ?p ?o ?g. }

• W3147572009 abstract "Abstract Background Drug-induced QTc prolongation (diQTP) is frequent and associated with a risk of sudden cardiac death. Identifying patients at risk of diQTP can enhance monitoring and treatment plans. Objective To develop a machine learning architecture for prediction of extreme diQTP (QTc >500ms OR ΔQTc >60ms) at the onset of treatment with a QTc prolonging drug. Methods We included 4,628 adult patients who received a baseline ECG within 6 months prior to treatment onset with a QTc prolonging drug and a follow-up ECG after the fifth dose. We collected known clinical QTc prolongation risk factors (CF). We developed a novel neural network architecture (QTNet) to predict diQTP from both the CF and baseline ECG data (ECGD), composed of both the ECG waveform and measurements (i.e. QTc),by fusing a state-of-the-art convolution neural network to process raw ECG waveforms with the CF using amulti-layer perceptron. We fit a logistic regression model using the CF, replicating RISQ-PATH as Baseline. We further compared the performance of QTNet (CF+ECGD) to neural network models trained using three variable subsets: a) baseline QTc (QTC-NN), b) CF-NN, and c) ECGD-NN. Results diQTP was present in 1030 patients (22.3%), of which baseline QTc was normal (QTc<450ms:Male/<470ms:Female) in 405 patients (39.3%). QTNet achieved the best performance (Figure 1)(AUROC, 0.802 [95% CI, 0.782-0.820]), outperforming predictions based on the Baseline (AUROC, 0.738 [95%CI, 0.716-0.757]), QTC-NN (AUROC, 0.735 [95% CI, 0.710-0.757]), CF-NN (AUROC, 0.778 [95% CI, 0.757-0.799]), and ECGD-NN (AUROC, 0.774 [95% CI, 0.750-0.794]). Conclusion We developed QTNet, the first deep learning model for predicting extreme diQTP, outperforming models trained on known clinical risk factors." @default.
• W3147572009 created "2021-04-13" @default.
• W3147572009 creator A5007411646 @default.
• W3147572009 creator A5022202456 @default.
• W3147572009 creator A5049341927 @default.
• W3147572009 creator A5049595189 @default.
• W3147572009 creator A5087660055 @default.
• W3147572009 creator A5091807315 @default.
• W3147572009 date "2021-03-26" @default.
• W3147572009 modified "2023-10-18" @default.
• W3147572009 title "QTNet: Predicting Drug-Induced QT prolongation with Deep Neural Networks" @default.
• W3147572009 cites W2334052098 @default.
• W3147572009 cites W2885511183 @default.
• W3147572009 cites W2902644322 @default.
• W3147572009 doi "https://doi.org/10.1101/2021.03.24.21254235" @default.
• W3147572009 hasPublicationYear "2021" @default.
• W3147572009 type Work @default.
• W3147572009 sameAs 3147572009 @default.
• W3147572009 citedByCount "0" @default.
• W3147572009 crossrefType "posted-content" @default.
• W3147572009 hasAuthorship W3147572009A5007411646 @default.
• W3147572009 hasAuthorship W3147572009A5022202456 @default.
• W3147572009 hasAuthorship W3147572009A5049341927 @default.
• W3147572009 hasAuthorship W3147572009A5049595189 @default.
• W3147572009 hasAuthorship W3147572009A5087660055 @default.
• W3147572009 hasAuthorship W3147572009A5091807315 @default.
• W3147572009 hasBestOaLocation W31475720091 @default.
• W3147572009 hasConcept C118441451 @default.
• W3147572009 hasConcept C126322002 @default.
• W3147572009 hasConcept C151956035 @default.
• W3147572009 hasConcept C164705383 @default.
• W3147572009 hasConcept C2775935837 @default.
• W3147572009 hasConcept C2777672339 @default.
• W3147572009 hasConcept C71924100 @default.
• W3147572009 hasConceptScore W3147572009C118441451 @default.
• W3147572009 hasConceptScore W3147572009C126322002 @default.
• W3147572009 hasConceptScore W3147572009C151956035 @default.
• W3147572009 hasConceptScore W3147572009C164705383 @default.
• W3147572009 hasConceptScore W3147572009C2775935837 @default.
• W3147572009 hasConceptScore W3147572009C2777672339 @default.
• W3147572009 hasConceptScore W3147572009C71924100 @default.
• W3147572009 hasLocation W31475720091 @default.
• W3147572009 hasOpenAccess W3147572009 @default.
• W3147572009 hasPrimaryLocation W31475720091 @default.
• W3147572009 hasRelatedWork W1975533718 @default.
• W3147572009 hasRelatedWork W1991508660 @default.
• W3147572009 hasRelatedWork W1997214226 @default.
• W3147572009 hasRelatedWork W2070623123 @default.
• W3147572009 hasRelatedWork W2086664958 @default.
• W3147572009 hasRelatedWork W2126603450 @default.
• W3147572009 hasRelatedWork W2130000151 @default.
• W3147572009 hasRelatedWork W2164262456 @default.
• W3147572009 hasRelatedWork W2371249220 @default.
• W3147572009 hasRelatedWork W4313444120 @default.
• W3147572009 isParatext "false" @default.
• W3147572009 isRetracted "false" @default.
• W3147572009 magId "3147572009" @default.
• W3147572009 workType "article" @default.