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Survival Analysis

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Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Identify and Estimate Survival in a Longitudinal Cohort of Patients With Lung Cancer.

JAMA network open
IMPORTANCE: Electronic health records (EHRs) provide a low-cost means of accessing detailed longitudinal clinical data for large populations. A lung cancer cohort assembled from EHR data would be a powerful platform for clinical outcome studies.

Alternative splicing associated with cancer stemness in kidney renal clear cell carcinoma.

BMC cancer
BACKGROUD: Cancer stemness is associated with metastases in kidney renal clear cell carcinoma (KIRC) and negatively correlates with immune infiltrates. Recent stemness evaluation methods based on the absolute expression have been proposed to reveal t...

A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods.

Annals of nuclear medicine
OBJECTIVE: This study evaluates the ability of several machine learning (ML) algorithms, developed using volumetric and texture data extracted from baseline F-FDG PET/CT studies performed initial staging of patient with esophageal cancer (EC), to pre...

Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance.

Nature communications
Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learnin...

Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning.

BMC endocrine disorders
INTRODUCTION: Recent studies have reported that HbA1c and lipid variability is useful for risk stratification in diabetes mellitus. The present study evaluated the predictive value of the baseline, subsequent mean of at least three measurements and v...

Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge.

PLoS computational biology
Advancements in sequencing have led to the proliferation of multi-omic profiles of human cells under different conditions and perturbations. In addition, many databases have amassed information about pathways and gene "signatures"-patterns of gene ex...

An Improved Muti-Task Learning Algorithm for Analyzing Cancer Survival Data.

IEEE/ACM transactions on computational biology and bioinformatics
Survival analysis is a popular branch of statistics. At present, many algorithms (like traditional multi-tasking learning model) cannot be applied well in practice because of censored data. Although using some model (like parametric regression model)...