Survival analysis holds a crucial role across diverse disciplines, such as economics, engineering and healthcare. It empowers researchers to analyze both time-invariant and time-varying data, encompassing phenomena like customer churn, material degradation and various medical outcomes. Given the complexity and heterogeneity of such data, recent endeavors have demonstrated successful integration of deep learning methodologies to address limitations in conventional statistical approaches. However, current methods typically involve cluttered probability distribution function (PDF), have lower sensitivity in censoring prediction, only model static datasets, or only rely on recurrent neural networks for dynamic modelling. In this paper, we propose a novel survival regression method capable of producing high-quality unimodal PDFs without any prior distribution assumption, by optimizing novel Margin-Mean-Variance loss and leveraging the flexibility of Transformer to handle both temporal and non-temporal data, coined UniSurv. Extensive experiments on several datasets demonstrate that UniSurv places a significantly higher emphasis on censoring compared to other methods.
Long-Term Pipeline Failure Prediction Using Nonparametric Survival Analysis
Data61-The Commonwealth Scientific and Industrial Research Organisation (CSIRO)（联邦科学与工业研究组织（CSIRO）Data61）；Monash University（莫纳什大学）；University of Technology Sydney（悉尼科技大学）
Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling
Goldsmiths, University of London（伦敦大学金史密斯学院）；School of Health Sciences, The University of Manchester（曼彻斯特大学健康科学学院）；Faculty of Computer Science and Engineering, Frankfurt University of Applied Sciences（法兰克福应用科学大学计算机科学与工程学院）；Institute of Psychiatry, Psychology and Neuroscience King’s College London（伦敦国王学院精神病学、心理学与神经科学研究所）
Bipartite Patient-Modality Graph Learning with Event-Conditional Modelling of Censoring for Cancer Survival Prediction
Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University（湖南省级生物信息学重点实验室，计算机科学与工程学院，中南大学）；Xinjiang Engineering Research Center of Big Data and Intelligent Software, School of Software, Xinjiang University（新疆大数据与智能软件工程研究中心，软件学院，新疆大学）
Modelling customer churn for the retail industry in a deep learning based sequential framework
University of Essex（埃塞克斯大学）；Profusion Media Ltd.（Profusion传媒有限公司）；Essex Business School（埃塞克斯商学院）；Friedrich-Alexander University Erlangen-Nuremberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
A Structured Nonparametric Framework for Nonlinear Accelerated Failure Time Models (KAN-AFT)
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology（数学系，先进科学学院，维洛雷理工学院）；Applied Statistics Unit, Indian Statistical Institute（应用统计单位，印度统计研究所）
Comprehensive Benchmarking of Machine Learning Methods for Risk Prediction Modelling from Large-Scale Survival Data: A UK Biobank Study
King’s College London（伦敦国王学院）；British Heart Foundation Centre of Research Excellence（英国心脏基金会卓越研究中心）；School of Cardiovascular & Metabolic Medicine and Sciences, King’s College London（伦敦国王学院心血管与代谢医学与科学学院）；Institute of Cardiovascular Science, University College London（伦敦大学学院心血管科学研究所）
该研究在 UK Biobank 大规模生存数据上基准测试八种线性至深度学习生存模型，发现 COX-PH 稳健，而复杂模型仅在特定样本与预测矩阵条件下占优。
