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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/gundemkorel/inventory_pricing_control
공식 코드Offline Dynamic Inventory and Pricing Strategy: Addressing Censored and Dependent Demand
https://github.com/hakimbenkirane/customics
공식 코드CustOmics: A versatile deep-learning based strategy for multi-omics integration
https://github.com/havakv/pycox
공식 코드Time-to-Event Prediction with Neural Networks and Cox Regression
https://github.com/helenypzhang/subspace-multimodal-learning
공식 코드Knowledge-driven Subspace Fusion and Gradient Coordination for Multi-modal Learning
https://github.com/hongliuuuuu/apl
공식 코드Adaptive Prototype Learning for Multimodal Cancer Survival Analysis
https://github.com/i6092467/vadesc
공식 코드https://github.com/ibm/dqs
공식 코드https://github.com/imatge-upc/survlimepy
공식 코드https://github.com/innse/motcat
공식 코드https://github.com/jacob-thrasher/te-ssl
공식 코드TE-SSL: Time and Event-aware Self Supervised Learning for Alzheimer's Disease Progression Analysis
https://github.com/jameslu01/Compute_HazRatio_ML
공식 코드https://github.com/jaredleekatzman/DeepSurv
공식 코드https://github.com/jcyang34/lspin
공식 코드https://github.com/jderiu/spot-the-bot-code
공식 코드Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue Systems
https://github.com/jeanselme/neuralfinegray
공식 코드Neural Fine-Gray: Monotonic neural networks for competing risks
https://github.com/jesse-islam/cbnn
공식 코드Case-Base Neural Networks: survival analysis with time-varying, higher-order interactions
https://github.com/jiaqima/soden
공식 코드SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks
https://github.com/jiaxiang-cheng/KAN-for-Survival-Analysis
공식 코드https://github.com/jsh0792/mcti
공식 코드Multimodal Cross-Task Interaction for Survival Analysis in Whole Slide Pathological Images
https://github.com/knottwill/CoxKAN
공식 코드CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival Analysis
https://github.com/konst-int-i/healnet
공식 코드HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data
https://github.com/labull/engineeringpatternrecognition
공식 코드https://github.com/lasso-net/lassonet
공식 코드https://github.com/li-lab-mcgill/mixehr-surg
공식 코드https://github.com/lilizhaoUM/DNNSurv
공식 코드DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values
https://github.com/lingjzhu/reddit_network
공식 코드The structure of online social networks modulates the rate of lexical change
https://github.com/linusbleistein/signature_survival
공식 코드https://github.com/liupei101/advmil
공식 코드AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Images
https://github.com/liupei101/vlsa
공식 코드https://github.com/liuyuaa/SurvUnc
공식 코드SurvUnc: A Meta-Model Based Uncertainty Quantification Framework for Survival Analysis
https://github.com/lurovi/survivalmultitree-pynsgp
공식 코드Interpretable Non-linear Survival Analysis with Evolutionary Symbolic Regression
https://github.com/majianthu/survival
공식 코드Copula Entropy based Variable Selection for Survival Analysis
https://github.com/mathoup31/simhawnet
공식 코드SimHawNet: A Modified Hawkes Process for Temporal Network Simulation
https://github.com/mcpathology/mrepath
공식 코드Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance
https://github.com/meixide/spinet
공식 코드Neural interval-censored survival regression with feature selection
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