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Survival Analysis

22 papers with code · Miscellaneous

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An Efficient Training Algorithm for Kernel Survival Support Vector Machines

21 Nov 2016sebp/scikit-survival

Survival analysis is a fundamental tool in medical research to identify predictors of adverse events and develop systems for clinical decision support.

SURVIVAL ANALYSIS

Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling

10 Jul 2017X-DataInitiative/tick

Tick is a statistical learning library for Python~3, with a particular emphasis on time-dependent models, such as point processes, and tools for generalized linear models and survival analysis.

POINT PROCESSES SURVIVAL ANALYSIS

DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network

2 Jun 2016jaredleekatzman/DeepSurv

We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient's covariates and treatment effectiveness in order to provide personalized treatment recommendations.

FEATURE ENGINEERING PREDICTING PATIENT OUTCOMES RECOMMENDATION SYSTEMS SURVIVAL ANALYSIS

The Brier Score under Administrative Censoring: Problems and Solutions

18 Dec 2019havakv/pycox

This administrative Brier score does not require estimation of the censoring distribution and is valid even if the censoring times can be identified from the covariates.

SURVIVAL ANALYSIS

Continuous and Discrete-Time Survival Prediction with Neural Networks

15 Oct 2019havakv/pycox

Application of discrete-time survival methods for continuous-time survival prediction is considered.

SURVIVAL ANALYSIS

Time-to-Event Prediction with Neural Networks and Cox Regression

1 Jul 2019havakv/pycox

New methods for time-to-event prediction are proposed by extending the Cox proportional hazards model with neural networks.

SURVIVAL ANALYSIS TIME-TO-EVENT PREDICTION

A Scalable Discrete-Time Survival Model for Neural Networks

2 May 2018havakv/pycox

It is important for predictive models to be able to use survival data, where each patient has a known follow-up time and event/censoring indicator.

SURVIVAL ANALYSIS

Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework

17 Jan 2018havakv/pycox

Survival analysis/time-to-event models are extremely useful as they can help companies predict when a customer will buy a product, churn or default on a loan, and therefore help them improve their ROI.

SURVIVAL ANALYSIS

Deep Recurrent Survival Analysis

7 Sep 2018rk2900/drsa

By capturing the time dependency through modeling the conditional probability of the event for each sample, our method predicts the likelihood of the true event occurrence and estimates the survival rate over time, i. e., the probability of the non-occurrence of the event, for the censored data.

SURVIVAL ANALYSIS

Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification

16 Nov 2017LeeJunHyun/The-Databases-for-Drug-Discovery

In this paper, we proposed a hybrid model, which integrates two key components 1) graph convolution neural network (graph CNN) and 2) relation network (RN).

SURVIVAL ANALYSIS