FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2745643704> ?p ?o ?g. }

• W2745643704 endingPage "705" @default.
• W2745643704 startingPage "703" @default.
• W2745643704 abstract "Cervical cancer remains the most common gynecological malignancy internationally, and nearly one third of patients succumb to this disease within 5 years from diagnosis.1Torre L.A. Bray F. Siegel R.L. Ferlay J. Lortet-Tieulent J. Jemal A. Global cancer statistics, 2012.CA Cancer J Clin. 2015; 65: 87-108Crossref PubMed Scopus (23667) Google Scholar, 2National Cancer Institute, Surveillance, Epidemiology, and End Results Program. Available at: https://seer.cancer.gov/statfacts/html/cervix.html. Accessed June 20, 2017.Google Scholar While patients with malignancy who have limited life expectancy benefit from less aggressive treatment intervention,3Mack J.W. Cronin A. Keating N.L. et al.Associations between end-of-life discussion characteristics and care received near death: a prospective cohort study.J Clin Oncol. 2012; 30: 4387-4395Crossref PubMed Scopus (374) Google Scholar there is currently little evidence to guide the prediction of the length of life expectancy. The aims of the study were (1) to examine predictors for survival by utilizing clinicolaboratory variables among women with recurrent cervical cancer and (2) to examine the utility of a new analytic approach using a deep-learning neural networks model. This institutional review board–approved retrospective study evaluated 157 women who developed recurrent cervical cancer among 431 women with cervical cancer diagnosed between January 2008 and December 2014. Prediction of 3 and 6 month survival after recurrence was compared between the historical approach (linear regression model) and experimental approach (deep-learning neural networks model) as described in our previous study.4LeCun Y. Bengio Y. Hinton G. Deep learning.Nature. 2015; 521: 436-444Crossref PubMed Scopus (42789) Google Scholar, 5Che Z. Purushotham S. Khemani R. Liu Y. Interpretable deep models for ICU outcome prediction.AMIA Annu Symp Proc. 2017; 2016: 371-380PubMed Google Scholar Model inputs included 13 unique variables (clinical: age, body habitus change, pain score, blood pressure [systolic and diastolic], and heart rate, and laboratory: white blood cell, hemoglobin, platelet, bicarbonate, blood urea nitrogen, creatinine, and albumin) from all outpatient visits following recurrence (5421 total data points). There were 102 women who died of cervical cancer (65.0%), with a median time to death after recurrence being 7.7 months. The deep-learning model had a significantly better prediction for 3 month (area under the curve, 0.747 vs 0.652, P < .0001) and 6 month (0.724 vs 0.685, P < .0001) survival compared with the linear regression model. In the deep-learning model, decreased 3 month survival was associated with older age, decreasing albumin level, decreasing body mass index, increasing pain score, decreasing systolic blood pressure, decreasing white blood cell count, increasing platelet counts, and decreasing hemoglobin levels determined as the interval changes from the baseline values at the recurrence (all, P < .05; Figure 1). Similar findings were seen in the 6 month survival prediction model. Our study suggests that deep learning by evaluating certain clinicolaboratory parameters commonly used in daily practice may be a useful approach to predict limited life expectancy in women with recurrent cervical cancer that can then translate into patient counseling regarding palliative care. Deep-learning models can be converted to a set of decision trees,5Che Z. Purushotham S. Khemani R. Liu Y. Interpretable deep models for ICU outcome prediction.AMIA Annu Symp Proc. 2017; 2016: 371-380PubMed Google Scholar whose rules may be more interpretable to the clinician (Figure 2). In this model, interval change of albumin was the most important factor to triage the patient. Then pain score and body habitus change need to be factored based on the albumin level. Based on the interval changes in these values, additional factors need to be evaluated for 3 month survival prediction." @default.
• W2745643704 created "2017-08-31" @default.
• W2745643704 creator A5000713007 @default.
• W2745643704 creator A5017164999 @default.
• W2745643704 creator A5017846156 @default.
• W2745643704 creator A5048524082 @default.
• W2745643704 creator A5058747937 @default.
• W2745643704 creator A5075566972 @default.
• W2745643704 creator A5081294498 @default.
• W2745643704 date "2017-12-01" @default.
• W2745643704 modified "2023-10-02" @default.
• W2745643704 title "A pilot study in using deep learning to predict limited life expectancy in women with recurrent cervical cancer" @default.
• W2745643704 cites W2135894244 @default.
• W2745643704 cites W2621053657 @default.
• W2745643704 cites W2917837889 @default.
• W2745643704 cites W2919115771 @default.
• W2745643704 doi "https://doi.org/10.1016/j.ajog.2017.08.012" @default.
• W2745643704 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7534808" @default.
• W2745643704 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28843741" @default.
• W2745643704 hasPublicationYear "2017" @default.
• W2745643704 type Work @default.
• W2745643704 sameAs 2745643704 @default.
• W2745643704 citedByCount "16" @default.
• W2745643704 countsByYear W27456437042018 @default.
• W2745643704 countsByYear W27456437042019 @default.
• W2745643704 countsByYear W27456437042020 @default.
• W2745643704 countsByYear W27456437042021 @default.
• W2745643704 countsByYear W27456437042022 @default.
• W2745643704 countsByYear W27456437042023 @default.
• W2745643704 crossrefType "journal-article" @default.
• W2745643704 hasAuthorship W2745643704A5000713007 @default.
• W2745643704 hasAuthorship W2745643704A5017164999 @default.
• W2745643704 hasAuthorship W2745643704A5017846156 @default.
• W2745643704 hasAuthorship W2745643704A5048524082 @default.
• W2745643704 hasAuthorship W2745643704A5058747937 @default.
• W2745643704 hasAuthorship W2745643704A5075566972 @default.
• W2745643704 hasAuthorship W2745643704A5081294498 @default.
• W2745643704 hasBestOaLocation W27456437041 @default.
• W2745643704 hasConcept C121608353 @default.
• W2745643704 hasConcept C126322002 @default.
• W2745643704 hasConcept C133925201 @default.
• W2745643704 hasConcept C143998085 @default.
• W2745643704 hasConcept C167135981 @default.
• W2745643704 hasConcept C17744445 @default.
• W2745643704 hasConcept C199539241 @default.
• W2745643704 hasConcept C2777740455 @default.
• W2745643704 hasConcept C2778220009 @default.
• W2745643704 hasConcept C2779399171 @default.
• W2745643704 hasConcept C2779473830 @default.
• W2745643704 hasConcept C2908647359 @default.
• W2745643704 hasConcept C29456083 @default.
• W2745643704 hasConcept C71924100 @default.
• W2745643704 hasConcept C74909509 @default.
• W2745643704 hasConcept C83867959 @default.
• W2745643704 hasConcept C99454951 @default.
• W2745643704 hasConceptScore W2745643704C121608353 @default.
• W2745643704 hasConceptScore W2745643704C126322002 @default.
• W2745643704 hasConceptScore W2745643704C133925201 @default.
• W2745643704 hasConceptScore W2745643704C143998085 @default.
• W2745643704 hasConceptScore W2745643704C167135981 @default.
• W2745643704 hasConceptScore W2745643704C17744445 @default.
• W2745643704 hasConceptScore W2745643704C199539241 @default.
• W2745643704 hasConceptScore W2745643704C2777740455 @default.
• W2745643704 hasConceptScore W2745643704C2778220009 @default.
• W2745643704 hasConceptScore W2745643704C2779399171 @default.
• W2745643704 hasConceptScore W2745643704C2779473830 @default.
• W2745643704 hasConceptScore W2745643704C2908647359 @default.
• W2745643704 hasConceptScore W2745643704C29456083 @default.
• W2745643704 hasConceptScore W2745643704C71924100 @default.
• W2745643704 hasConceptScore W2745643704C74909509 @default.
• W2745643704 hasConceptScore W2745643704C83867959 @default.
• W2745643704 hasConceptScore W2745643704C99454951 @default.
• W2745643704 hasIssue "6" @default.
• W2745643704 hasLocation W27456437041 @default.
• W2745643704 hasLocation W27456437042 @default.
• W2745643704 hasLocation W27456437043 @default.
• W2745643704 hasOpenAccess W2745643704 @default.
• W2745643704 hasPrimaryLocation W27456437041 @default.
• W2745643704 hasRelatedWork W1998885523 @default.
• W2745643704 hasRelatedWork W2081064592 @default.
• W2745643704 hasRelatedWork W2384708512 @default.
• W2745643704 hasRelatedWork W2385052163 @default.
• W2745643704 hasRelatedWork W2514884906 @default.
• W2745643704 hasRelatedWork W3208090289 @default.
• W2745643704 hasRelatedWork W3210359220 @default.
• W2745643704 hasRelatedWork W4211002539 @default.
• W2745643704 hasRelatedWork W4298630154 @default.
• W2745643704 hasRelatedWork W4311496968 @default.
• W2745643704 hasVolume "217" @default.
• W2745643704 isParatext "false" @default.
• W2745643704 isRetracted "false" @default.
• W2745643704 magId "2745643704" @default.
• W2745643704 workType "article" @default.