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Global Heart Sep 2018
Review
Topics: Africa South of the Sahara; Age Distribution; Cardiovascular Diseases; Humans; Morbidity; Risk Assessment; Risk Factors; Sex Distribution; Survival Rate
PubMed: 30360788
DOI: 10.1016/j.gheart.2018.09.514 -
Global Heart Sep 2018
Review
Topics: Age Distribution; Cardiovascular Diseases; Humans; Morbidity; North America; Risk Assessment; Sex Distribution; Survival Rate
PubMed: 30360789
DOI: 10.1016/j.gheart.2018.09.515 -
Global Heart Sep 2018
Review
Topics: Asia; Cardiovascular Diseases; Developing Countries; Humans; Morbidity; Poverty; Risk Factors; Socioeconomic Factors; Survival Rate
PubMed: 30360795
DOI: 10.1016/j.gheart.2018.09.521 -
Global Heart Sep 2018
Review
Topics: Age Factors; Asia, Southeastern; Cardiovascular Diseases; Humans; Morbidity; Risk Assessment; Sex Factors; Survival Rate
PubMed: 30360794
DOI: 10.1016/j.gheart.2018.09.520 -
The Annals of Thoracic Surgery Mar 2011
Topics: Cardiac Surgical Procedures; Follow-Up Studies; Heart Neoplasms; Humans; Sarcoma; Survival Rate; Treatment Outcome; United States
PubMed: 21352996
DOI: 10.1016/j.athoracsur.2010.12.003 -
Methods (San Diego, Calif.) Aug 2021The survival rate of cancer has increased significantly during the past two decades for breast, prostate, testicular, and colon cancer, while the brain and pancreatic...
The survival rate of cancer has increased significantly during the past two decades for breast, prostate, testicular, and colon cancer, while the brain and pancreatic cancers have a much lower median survival rate that has not improved much over the last forty years. This has imposed the challenge of finding gene markers for early cancer detection and treatment strategies. Different methods including regression-based Cox-PH, artificial neural networks, and recently deep learning algorithms have been proposed to predict the survival rate for cancers. We established in this work a novel graph convolution neural network (GCNN) approach called Surv_GCNN to predict the survival rate for 13 different cancer types using the TCGA dataset. For each cancer type, 6 Surv_GCNN models with graphs generated by correlation analysis, GeneMania database, and correlation + GeneMania were trained with and without clinical data to predict the risk score (RS). The performance of the 6 Surv_GCNN models was compared with two other existing models, Cox-PH and Cox-nnet. The results showed that Cox-PH has the worst performance among 8 tested models across the 13 cancer types while Surv_GCNN models with clinical data reported the best overall performance, outperforming other competing models in 7 out of 13 cancer types including BLCA, BRCA, COAD, LUSC, SARC, STAD, and UCEC. A novel network-based interpretation of Surv_GCNN was also proposed to identify potential gene markers for breast cancer. The signatures learned by the nodes in the hidden layer of Surv_GCNN were identified and were linked to potential gene markers by network modularization. The identified gene markers for breast cancer have been compared to a total of 213 gene markers from three widely cited lists for breast cancer survival analysis. About 57% of gene markers obtained by Surv_GCNN with correlation + GeneMania graph either overlap or directly interact with the 213 genes, confirming the effectiveness of the identified markers by Surv_GCNN.
Topics: Algorithms; Breast Neoplasms; Humans; Male; Neural Networks, Computer; Survival Rate
PubMed: 33484826
DOI: 10.1016/j.ymeth.2021.01.004 -
American Family Physician Mar 2018
Review
Topics: Global Health; Hospice Care; Humans; Life Expectancy; Neoplasms; Survival Rate
PubMed: 29671507
DOI: No ID Found -
CMAJ : Canadian Medical Association... Sep 2017
Review
Topics: Disease Management; Global Health; Humans; Morbidity; Sepsis; Survival Rate
PubMed: 28893874
DOI: 10.1503/cmaj.171008 -
Korean Journal of Radiology May 2022
Topics: Humans; Research Design; Survival Rate
PubMed: 35506526
DOI: 10.3348/kjr.2022.0061 -
Clinical Cardiology Oct 2008I don't make any claims to be an expert in managing patients after cardiac transplantation, but I do recall 2 patients which I believe make the point that times have...
I don't make any claims to be an expert in managing patients after cardiac transplantation, but I do recall 2 patients which I believe make the point that times have changed since the beginning of cardiac transplantation. In 1968, a patient of mine underwent cardiac transplantation. This was the first transplantation done at Johns Hopkins, and I believe it was 99(th) in the world. This patient survived the surgery very well, left the hospital, and in 4 wk he was dead.
Topics: Heart Failure; Heart Transplantation; Humans; Prognosis; Survival Rate
PubMed: 18855348
DOI: 10.1002/clc.20440