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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/mi2datalab/interpret-time-to-event
공식 코드Interpretable machine learning for time-to-event prediction in medicine and healthcare
https://github.com/mi2datalab/xlungs-trustworthy-los-prediction
공식 코드Interpretable machine learning for time-to-event prediction in medicine and healthcare
https://github.com/modeloriented/forester
공식 코드https://github.com/modeloriented/survex
공식 코드Interpretable machine learning for time-to-event prediction in medicine and healthcare
https://github.com/moothes/ccl-survival
공식 코드Cohort-Individual Cooperative Learning for Multimodal Cancer Survival Analysis
https://github.com/msesia/conformal_survival
공식 코드Doubly Robust Conformalized Survival Analysis with Right-Censored Data
https://github.com/mu-data-science/gaf
공식 코드Predicting Survivability of Cancer Patients with Metastatic Patterns Using Explainable AI
https://github.com/nliulab/FedScore
공식 코드Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data
https://github.com/novartis/torchsurv
공식 코드https://github.com/ntailab/gradient_growing_trees
공식 코드A novel gradient-based method for decision trees optimizing arbitrary differential loss functions
https://github.com/ntailab/isurvmqj
공식 코드Survival Analysis as Imprecise Classification with Trainable Kernels
https://github.com/paidamoyo/adversarial_time_to_event
공식 코드https://github.com/paidamoyo/survival_cluster_analysis
공식 코드https://github.com/papachristoumarios/dp-social-learning
공식 코드https://github.com/patricia-a-apellaniz/savae
공식 코드SAVAE: Leveraging the variational Bayes autoencoder for survival analysis
https://github.com/peelen-mark/identifying-cancer-subtypes-code
공식 코드https://github.com/pkeller00/histokernel
공식 코드https://github.com/rahulk207/dagsurv
공식 코드DAGSurv: Directed Acyclic Graph Based Survival Analysis Using Deep Neural Networks
https://github.com/rajesh-lab/X-CAL
공식 코드https://github.com/rajesh-lab/inverse-weighted-survival-games
공식 코드https://github.com/ransulab/eocprognosis
공식 코드EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological Images
https://github.com/raphaels1/distribution_discrimination
공식 코드Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures
https://github.com/ratschlab/tls
공식 코드https://github.com/raymvp/hacsurv
공식 코드HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks
https://github.com/rk2900/DLF
공식 코드Deep Landscape Forecasting for Real-time Bidding Advertising
https://github.com/rk2900/drsa
공식 코드https://github.com/rmarceauwest/fiveSTAR
공식 코드https://github.com/rohansangave/SurvivalAnalysis
공식 코드https://github.com/rubikloud/matrnn
공식 코드Multivariate Arrival Times with Recurrent Neural Networks for Personalized Demand Forecasting
https://github.com/ruizhang1996/optimal-sparse-survival-trees-public
공식 코드https://github.com/sdittmer/survival_analysis_sumo_plus_plus
공식 코드Reinterpreting survival analysis in the universal approximator age
https://github.com/sehwankimstat/SCENE
공식 코드Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event Data
https://github.com/shaker82/mssda
공식 코드https://github.com/shi-ang/csd
공식 코드Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration
https://github.com/slds-lmu/paper_2023_survival_benchmark
공식 코드A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data
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