Survival Analysis
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Survival Analysis is a branch of statistics focused on the study of time-to-event data, usually called survival times. This type of data appears in a wide range of applications such as failure times in mechanical systems, death times of patients in a clinical trial or duration of unemployment in a population. One of the main objectives of Survival Analysis is the estimation of the so-called survival function and the hazard function. If a random variable has density function $f$ and cumulative distribution function $F$, then its survival function $S$ is $1-F$, and its hazard $λ$ is $f/S$.
Source: Gaussian Processes for Survival Analysis
Image: Kvamme et al.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/JJ-ZHOU-Code/RobustMultiModel
https://github.com/MolecularAI/uq4dd
Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labels
https://github.com/Museau/Survival_Pytorch_EMD
https://github.com/X-DataInitiative/tick_archive
https://github.com/autonlab/auton-survival
https://github.com/avinashbarnwal/Synthetic-Data
https://github.com/carlr67/deephitplus
Feature Selection for Survival Analysis with Competing Risks using Deep Learning
https://github.com/chiragnagpal/DeepSurvivalMachines
https://github.com/czifan/DeepSurv.pytorch
https://github.com/georgehc/npsurvival
https://github.com/ilemhadri/lassoNet
Neural interval-censored survival regression with feature selection
https://github.com/jimmyhu4/fraud-detection
SAFE: A Neural Survival Analysis Model for Fraud Early Detection
https://github.com/jxieeducation/Quick-Data-Science-Experiments-2019
Deep Landscape Forecasting for Real-time Bidding Advertising
https://github.com/konst-int-i/mm-lego
HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data
https://github.com/leondepf/SAFE
SAFE: A Neural Survival Analysis Model for Fraud Early Detection
https://github.com/leondepf/SurvivalNet
Learning Genomic Representations to Predict Clinical Outcomes in Cancer
https://github.com/loft-br/xgboost-survival-embeddings
The Brier Score under Administrative Censoring: Problems and Solutions
https://github.com/mkazmier/torchmtlr
Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework
https://github.com/paidamoyo/counterfactual_survival_analysis
https://github.com/qinzzz/auton-survival-785
https://github.com/sebp/scikit-survival
An Efficient Training Algorithm for Kernel Survival Support Vector Machines
https://github.com/sschrod/bites
https://github.com/stefangroha/SurvNODE
A General Framework for Survival Analysis and Multi-State Modelling
https://github.com/yishuwei2019/DL-based-dynamic-risk-prediciton
Deep Learning for Patient-Specific Kidney Graft Survival Analysis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.