survival prediction
Robust pathway-based multi-omics data integration using directed random walks for survival prediction in multiple cancer studies
... A denoising algorithm based on relevance network topology (DART) integrates existing pathways with gene expression data by deriving perturbation signatures which reflect gene contributions in each pathway to ob- tain ...
13
Assessment of performance of survival prediction models for cancer prognosis
... Cancer survival studies are commonly analyzed using survival-time prediction models for cancer ...actual survival time, but these metrics can sometimes ...a survival-time threshold, and ...
11
Integrative pathway-based survival prediction utilizing the interaction between gene expression and DNA methylation in breast cancer
... two approaches: one that adds bi-directional edges be- tween expression and methylation features of the same gene that has both profiles, and another that considers only the anti-correlated interactions between the ...
11
Outcomes and survival prediction models for severe adult acute respiratory distress syndrome treated with extracorporeal membrane oxygenation
... of survival prediction models, and we cannot rule out that other pre-ECMO items not collected in these databases might also impact on the ...for survival prediction in a general population of ...
10
Gene Expression with Pheonotype Classification and Patient Survival Prediction Algorithm
... patient survival prediction from gene expression profiles, and the problems of functional site prediction from DNA ...patient survival prediction, we present a new idea of selecting ...
6
IGHV gene mutational status and 17p deletion are independent molecular predictors in a comprehensive clinical biological prognostic model for overall survival prediction in chronic lymphocytic leukemia
... (stage, LNR, sex, age) plus two quantitative assays (ALC, b2 M) [3,4,8]. Two independent studies [4,8] failed to con- firm the predictive power of ALC. A simplification of the PI from six to four variables was previously ...
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A comparison of machine learning techniques for survival prediction in breast cancer
... It is well know that the fitness function driving the evolutionary dynamics in a GP fra- mework can be modified in order to let emerge solutions with different characteristics. The results presented and discussed in the ...
13
Survival prediction of stage I lung adenocarcinomas by expression of 10 genes
... Analysis of an in vitro–derived transcriptional signature. We have previ- ously shown that a biased approach to cancer transcriptomes can lead to the identification of cancer signatures (25). In particular, a 28-gene ...
10
Incidence trends and survival prediction of hepatoblastoma in children: a population-based study
... In the present study, we constructed a nomogram based on the results of multivariate analysis and included common clinical factors in the model. Our nomogram performed well in predicting OS, and its prediction was ...
9
New Trauma and Injury Severity Score (TRISS) adjustments for survival prediction
... exclude the presumably more serious patients from the analysis of survival probability (intubated) [10, 17, 18]. A literature review of comparative studies of the original TRISS with modified models showed an ...
6
Patient Survival Prediction Using Data Mining Technique
... Kaplan-Meier survival bend, whilst a affected person is edited, the bend does not step down because it does while a affected person bites the dust; instead, a tick imprint is for the most component used to ...
8
Prognostic indicators of survival and survival prediction model following extracorporeal cardiopulmonary resuscitation in patients with sudden refractory cardiac arrest
... 24-hour survival, the presence of ROSC ≥ 20 min, hos- pital length of stay (LOS), survival to hospital discharge, Glasgow–Pittsburgh cerebral performance category (CPC) score at discharge, and the final ...
10
ML Based HCC Survival Prediction System
... After cleaning the data, the next job in hand was to transform the data. Each feature in the dataset had a different range. If not scaled, the features with higher range would bias the prediction process thus ...
5
Implementation of a rapid learning platform: Predicting 2-year survival in laryngeal carcinoma patients in a clinical setting
... univariate survival analysis, the Kaplan Meier method was ...lowest survival predictions and 25% highest survival predictions as the poor and good prognosis group ...These survival ...
9
A novel molecular and clinical staging model to predict survival for patients with esophageal squamous cell carcinoma
... a survival prediction model for prognostication in patients with ...The survival prediction model was constructed based on cluster and discriminant analyses in a training cohort (N=205), and ...
11
Comparison of Artificial Neural Networks and Cox Regression Models in Prediction of Kidney Transplant Survival
... the survival of the patients with gastric cancer” in 2010, data for 436 patients referring to the Taleghani Hospital in Semnan for surgical operation from 2002 through 2006, was ...better prediction ...
9
<p>Homogeneity in prediction of survival probabilities for subcategories of hipprosthesis data: the Nordic Arthroplasty Register Association, 2000–2013</p>
... estimate based on the NARA data. Therefore, for sam- ples >1,460, the survival-probability estimate based on a Norwegian sample is superior with regard to precision and accuracy compared to the NARA estimate. ...
6
Usefulness of CTP score and MELD score in listing patients for liver transplantation.
... The complex issues of optimal indications for transplantation and prioritization for the allocation of liver grafts have been the main incentives to the development and widespread diffusion of MELD score. However, it ...
96
Recent Trends in Computational Prediction of Renal Transplantation Outcomes
... graft survival rate analysis using the immunosuppressant drug (cyclosporine) after renal transplantation in both long duration (more than 1 year) and short duration (less than 1 year) and he used a regression ...
5
A Boltzmann-Tsallis approach towards cell survival curve in Radiobiology
... cell survival curve at low dose using tools of extensive Statistical Mechanics and nonextensive Statistical ...cell survival curve and the canonical ensemble formalism based on Boltzmann Gibbs statistic and ...
5