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Survival Analysis

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Survival Analysis of COVID-19 Patients in Russia Using Machine Learning.

Studies in health technology and informatics
The current pandemic can likely have several waves and will require a major effort to save lives and provide optimal treatment. The efficient clinical resource planning and efficient treatment require identification of risk groups and specific clinic...

Improved survival analysis by learning shared genomic information from pan-cancer data.

Bioinformatics (Oxford, England)
MOTIVATION: Recent advances in deep learning have offered solutions to many biomedical tasks. However, there remains a challenge in applying deep learning to survival analysis using human cancer transcriptome data. As the number of genes, the input v...

External Validation of PATHFx Version 3.0 in Patients Treated Surgically and Nonsurgically for Symptomatic Skeletal Metastases.

Clinical orthopaedics and related research
BACKGROUND: PATHFx is a clinical decision-support tool based on machine learning capable of estimating the likelihood of survival after surgery for patients with skeletal metastases. The applicability of any machine-learning tool depends not only on ...

Predicting Cognitive Impairment and Dementia: A Machine Learning Approach.

Journal of Alzheimer's disease : JAD
BACKGROUND: Efforts to identify important risk factors for cognitive impairment and dementia have to date mostly relied on meta-analytic strategies. A comprehensive empirical evaluation of these risk factors within a single study is currently lacking...

PAGE-Net: Interpretable and Integrative Deep Learning for Survival Analysis Using Histopathological Images and Genomic Data.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
The integration of multi-modal data, such as histopathological images and genomic data, is essential for understanding cancer heterogeneity and complexity for personalized treatments, as well as for enhancing survival predictions in cancer study. His...

A Deep Learning Framework for Predicting Response to Therapy in Cancer.

Cell reports
A major challenge in cancer treatment is predicting clinical response to anti-cancer drugs on a personalized basis. Using a pharmacogenomics database of 1,001 cancer cell lines, we trained deep neural networks for prediction of drug response and asse...

Optimization of treatment strategy by using a machine learning model to predict survival time of patients with malignant glioma after radiotherapy.

Journal of radiation research
The purpose of this study was to predict the survival time of patients with malignant glioma after radiotherapy with high accuracy by considering additional clinical factors and optimize the prescription dose and treatment duration for individual pat...

Multidimensional Sleep and Mortality in Older Adults: A Machine-Learning Comparison With Other Risk Factors.

The journals of gerontology. Series A, Biological sciences and medical sciences
BACKGROUND: Sleep characteristics related to duration, timing, continuity, and sleepiness are associated with mortality in older adults, but rarely considered in health recommendations. We applied machine learning to: (i) establish the predictive abi...

[French ccAFU guidelines – Update 2018–2020: Bladder cancer].

Progres en urologie : journal de l'Association francaise d'urologie et de la Societe francaise d'urologie
OBJECTIVE: To propose updated French guidelines for non-muscle invasive (NMIBC) and muscle-invasive (MIBC) bladder cancers.

web-rMKL: a web server for dimensionality reduction and sample clustering of multi-view data based on unsupervised multiple kernel learning.

Nucleic acids research
More and more affordable high-throughput techniques for measuring molecular features of biomedical samples have led to a huge increase in availability and size of different types of multi-omic datasets, containing, for example, genetic or histone mod...