FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4377012520> ?p ?o ?g. }

• W4377012520 endingPage "e466" @default.
• W4377012520 startingPage "e458" @default.
• W4377012520 abstract "Recurrent graft fibrosis after liver transplantation can threaten both graft and patient survival. Therefore, early detection of fibrosis is essential to avoid disease progression and the need for retransplantation. Non-invasive blood-based biomarkers of fibrosis are limited by moderate accuracy and high cost. We aimed to evaluate the accuracy of machine learning algorithms in detecting graft fibrosis using longitudinal clinical and laboratory data.In this retrospective, longitudinal study, we trained machine learning algorithms, including our novel weighted long short-term memory (LSTM) model, to predict the risk of significant fibrosis using follow-up data from 1893 adults who had a liver transplantation between Feb 1, 1987, and Dec 30, 2019, with at least one liver biopsy post transplantation. Liver biopsy samples with indefinitive fibrosis stage and those from patients with multiple transplantations were excluded. Longitudinal clinical variables were collected from transplantation to the date of last available liver biopsy. Deep learning models were trained on 70% of the patients as the training set and 30% of the patients as the test set. The algorithms were also separately tested on longitudinal data from patients in a subgroup of patients (n=149) who had transient elastography within 1 year before or after the date of liver biopsy. Weighted LSTM model performance for diagnosing significant fibrosis was compared against LSTM, other deep learning models (recurrent neural network and temporal convolutional network), and machine learning models (Random Forest, Support vector machines, Logistic regression, Lasso regression, and Ridge regression) and aspartate aminotransferase-to-platelet ratio index (APRI), fibrosis-4 index (FIB-4), and transient elastography.1893 people who had a liver transplantation (1261 [67%] men and 632 [33%] women) with at least one liver biopsy between Jan 1, 1992, and June 30, 2020, were included in the study (591 [31%] cases and 1302 [69%] controls). The median age at liver transplantation was 53·7 years (IQR 47·3-59·0) for cases and 55·3 years (48·0 to 61·2) for controls. The median time interval between transplant and liver biopsy was 21 months (5 to 71). The weighted LSTM model (area under the curve 0·798 [95% CI 0·790 to 0·810]) consistently outperformed other methods, including unweighted LSTM (0·761 [0·750 to 0·769]; p=0·031) Recurrent Neural Network (0·736 [0·721 to 0·744]), Temporal Convolutional Networks (0·700 [0·662 to 0·747], and Random Forest 0·679 [0·652 to 0·707]), FIB-4 (0·650 [0·636 to 0·663]) and APRI (0·682 [0·671 to 0·694]) when diagnosing F2 or worse stage fibrosis. In a subgroup of patients with transient elastography results, weighted LSTM was not significantly better at detecting fibrosis (≥F2; 0·705 [0·687 to 0·724]) than transient elastography (0·685 [0·662 to 0·704]). The top ten variables predictive for significant fibrosis were recipient age, primary indication for transplantation, donor age, and longitudinal data for creatinine, alanine aminotransferase, aspartate aminotransferase, total bilirubin, platelets, white blood cell count, and weight.Deep learning algorithms, particularly weighted LSTM, outperform other routinely used non-invasive modalities and could help with the earlier diagnosis of graft fibrosis using longitudinal clinical and laboratory variables. The list of most important predictive variables for the development of fibrosis will enable clinicians to modify their management accordingly to prevent onset of graft cirrhosis.Canadian Institute of Health Research, American Society of Transplantation, Toronto General and Western Hospital Foundation, and Paladin Labs." @default.
• W4377012520 created "2023-05-19" @default.
• W4377012520 creator A5005136927 @default.
• W4377012520 creator A5006281428 @default.
• W4377012520 creator A5007217781 @default.
• W4377012520 creator A5022291970 @default.
• W4377012520 creator A5031077858 @default.
• W4377012520 creator A5037639974 @default.
• W4377012520 creator A5038158891 @default.
• W4377012520 creator A5040169308 @default.
• W4377012520 creator A5040175993 @default.
• W4377012520 creator A5047524751 @default.
• W4377012520 creator A5049168767 @default.
• W4377012520 creator A5050357012 @default.
• W4377012520 creator A5070701508 @default.
• W4377012520 creator A5080869073 @default.
• W4377012520 creator A5081545818 @default.
• W4377012520 date "2023-07-01" @default.
• W4377012520 modified "2023-10-18" @default.
• W4377012520 title "A deep learning framework for personalised dynamic diagnosis of graft fibrosis after liver transplantation: a retrospective, single Canadian centre, longitudinal study" @default.
• W4377012520 cites W1634189574 @default.
• W4377012520 cites W1945442937 @default.
• W4377012520 cites W1993332611 @default.
• W4377012520 cites W1997865776 @default.
• W4377012520 cites W2013354166 @default.
• W4377012520 cites W2014426026 @default.
• W4377012520 cites W2016393423 @default.
• W4377012520 cites W2048613643 @default.
• W4377012520 cites W2064387090 @default.
• W4377012520 cites W2064675550 @default.
• W4377012520 cites W2094174828 @default.
• W4377012520 cites W2102284866 @default.
• W4377012520 cites W2114376411 @default.
• W4377012520 cites W2144351060 @default.
• W4377012520 cites W2328176404 @default.
• W4377012520 cites W2513682679 @default.
• W4377012520 cites W2757152924 @default.
• W4377012520 cites W2908318651 @default.
• W4377012520 cites W2969698871 @default.
• W4377012520 cites W2974347135 @default.
• W4377012520 cites W3016224791 @default.
• W4377012520 cites W3032764835 @default.
• W4377012520 cites W3119555742 @default.
• W4377012520 cites W3175203932 @default.
• W4377012520 cites W3208685146 @default.
• W4377012520 doi "https://doi.org/10.1016/s2589-7500(23)00068-7" @default.
• W4377012520 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37210229" @default.
• W4377012520 hasPublicationYear "2023" @default.
• W4377012520 type Work @default.
• W4377012520 citedByCount "2" @default.
• W4377012520 countsByYear W43770125202023 @default.
• W4377012520 crossrefType "journal-article" @default.
• W4377012520 hasAuthorship W4377012520A5005136927 @default.
• W4377012520 hasAuthorship W4377012520A5006281428 @default.
• W4377012520 hasAuthorship W4377012520A5007217781 @default.
• W4377012520 hasAuthorship W4377012520A5022291970 @default.
• W4377012520 hasAuthorship W4377012520A5031077858 @default.
• W4377012520 hasAuthorship W4377012520A5037639974 @default.
• W4377012520 hasAuthorship W4377012520A5038158891 @default.
• W4377012520 hasAuthorship W4377012520A5040169308 @default.
• W4377012520 hasAuthorship W4377012520A5040175993 @default.
• W4377012520 hasAuthorship W4377012520A5047524751 @default.
• W4377012520 hasAuthorship W4377012520A5049168767 @default.
• W4377012520 hasAuthorship W4377012520A5050357012 @default.
• W4377012520 hasAuthorship W4377012520A5070701508 @default.
• W4377012520 hasAuthorship W4377012520A5080869073 @default.
• W4377012520 hasAuthorship W4377012520A5081545818 @default.
• W4377012520 hasBestOaLocation W43770125201 @default.
• W4377012520 hasConcept C108583219 @default.
• W4377012520 hasConcept C119857082 @default.
• W4377012520 hasConcept C126322002 @default.
• W4377012520 hasConcept C141071460 @default.
• W4377012520 hasConcept C151956035 @default.
• W4377012520 hasConcept C154945302 @default.
• W4377012520 hasConcept C167135981 @default.
• W4377012520 hasConcept C2775934546 @default.
• W4377012520 hasConcept C2777766500 @default.
• W4377012520 hasConcept C2779609443 @default.
• W4377012520 hasConcept C2780559512 @default.
• W4377012520 hasConcept C2911091166 @default.
• W4377012520 hasConcept C41008148 @default.
• W4377012520 hasConcept C50382708 @default.
• W4377012520 hasConcept C71924100 @default.
• W4377012520 hasConceptScore W4377012520C108583219 @default.
• W4377012520 hasConceptScore W4377012520C119857082 @default.
• W4377012520 hasConceptScore W4377012520C126322002 @default.
• W4377012520 hasConceptScore W4377012520C141071460 @default.
• W4377012520 hasConceptScore W4377012520C151956035 @default.
• W4377012520 hasConceptScore W4377012520C154945302 @default.
• W4377012520 hasConceptScore W4377012520C167135981 @default.
• W4377012520 hasConceptScore W4377012520C2775934546 @default.
• W4377012520 hasConceptScore W4377012520C2777766500 @default.
• W4377012520 hasConceptScore W4377012520C2779609443 @default.
• W4377012520 hasConceptScore W4377012520C2780559512 @default.
• W4377012520 hasConceptScore W4377012520C2911091166 @default.
• W4377012520 hasConceptScore W4377012520C41008148 @default.
• W4377012520 hasConceptScore W4377012520C50382708 @default.
• W4377012520 hasConceptScore W4377012520C71924100 @default.

• next