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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/ssokota/spie
공식 코드Simultaneous Prediction Intervals for Patient-Specific Survival Curves
https://github.com/survival-org/dl4survival
공식 코드https://github.com/survival-org/scoring-rules-2024
공식 코드https://github.com/sveinnpalsson/semivaebrats
공식 코드Semi-Supervised Variational Autoencoder for Survival Prediction
https://github.com/teapearce/censored_quantile_regression_nn
공식 코드Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis
https://github.com/teboozas/dart_ecai23
공식 코드Towards Flexible Time-to-event Modeling: Optimizing Neural Networks via Rank Regression
https://github.com/thecml/DependentEVAL
공식 코드Overcoming Dependent Censoring in the Evaluation of Survival Models
https://github.com/thecml/ball-bearing-survival
공식 코드Predicting Survival Time of Ball Bearings in the Presence of Censoring
https://github.com/thecml/baysurv
공식 코드Efficient Training of Probabilistic Neural Networks for Survival Analysis
https://github.com/thecml/mensa
공식 코드MENSA: A Multi-Event Network for Survival Analysis under Informative Censoring
https://github.com/thecml/rulsurv
공식 코드A probabilistic estimation of remaining useful life from censored time-to-event data
https://github.com/thinkxca/RUL
공식 코드Survival Analysis with Machine Learning for Predicting Li-ion Battery Remaining Useful Life
https://github.com/tissueimageanalytics/conic
공식 코드https://github.com/tomer1812/discretetimesurvivalpenalization
공식 코드Discrete-time Competing-Risks Regression with or without Penalization
https://github.com/tomer1812/pydts
공식 코드PyDTS: A Python Package for Discrete-Time Survival (Regularized) Regression with Competing Risks
https://github.com/tum-camp/survival-support-vector-machine
공식 코드An Efficient Training Algorithm for Kernel Survival Support Vector Machines
https://github.com/udellgroup/dnamite
공식 코드DNAMite: Interpretable Calibrated Survival Analysis with Discretized Additive Models
https://github.com/vanderschaarlab/mlforhealthlabpub/tree/main/alg/dynamic_disease_network_ddp
공식 코드https://github.com/vanderschaarlab/survivalgan
공식 코드SurvivalGAN: Generating Time-to-Event Data for Survival Analysis
https://github.com/vironix-health/CKD-ZD-project
공식 코드https://github.com/vollmer-lab/survival_fairness
공식 코드https://github.com/weijiazhang24/dcsurvival
공식 코드Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability Guarantees
https://github.com/xiaobin-xs/EEG-competing-risks
공식 코드https://github.com/xintianhan/survival-mdn
공식 코드https://github.com/xinz0419/unisurv
공식 코드Adaptive Transformer Modelling of Density Function for Nonparametric Survival Analysis
https://github.com/xs018/nsotree
공식 코드https://github.com/xs018/survrelu
공식 코드SurvReLU: Inherently Interpretable Survival Analysis via Deep ReLU Networks
https://github.com/yonghaozhao722/tsf
공식 코드https://github.com/zhangquan-ut/Lomax-delegate-racing-for-survival-analysis-with-competing-risks
공식 코드Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks
https://github.com/zhimeir/cfsurv_paper
공식 코드https://github.com/zhimeir/cfsurvival
공식 코드https://github.com/zkeggia/dsa_refactor
공식 코드https://github.com/zzs95/RSP_COVID
공식 코드Region-specific Risk Quantification for Interpretable Prognosis of COVID-19
https://gitlab.com/hylkedonker/harmonium-models
공식 코드https://github.com/EbnulMahmood/PatternLab
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.