This paper introduces a novel non-parametric deep model for estimating time-to-event (survival analysis) in presence of censored data and competing risks. The model is designed based on the sequence-to-sequence (Seq2Seq) architecture, therefore we name it Survival Seq2Seq. The first recurrent neural network (RNN) layer of the encoder of our model is made up of Gated Recurrent Unit with Decay (GRU-D) cells. These cells have the ability to effectively impute not-missing-at-random values of longitudinal datasets with very high missing rates, such as electronic health records (EHRs). The decoder of Survival Seq2Seq generates a probability distribution function (PDF) for each competing risk without assuming any prior distribution for the risks. Taking advantage of RNN cells, the decoder is able to generate smooth and virtually spike-free PDFs. This is beyond the capability of existing non-parametric deep models for survival analysis. Training results on synthetic and medical datasets prove that Survival Seq2Seq surpasses other existing deep survival models in terms of the accuracy of predictions and the quality of generated PDFs.
Extending the Neural Additive Model for Survival Analysis with EHR Data
Massachusetts Institute of Technology（麻省理工学院）；Enolink Inc.（Enolink公司）；Seoul National University Hospital Healthcare System Gangnam Center（首尔大学医院医疗系统江南中心）
Prediction of Survival Outcomes under Clinical Presence Shift: A Joint Neural Network Architecture
Vincent Jeanselme Department of Biomedical Informatics, Columbia University MRC Biostatistics Unit, University of Cambridge Glen Martin Matthew Sperrin Division of Informatics, Imaging \& Data Sciences, University of Manchester Niels Peek The Healthcare Improvement Studies (THIS) Institute, University of Cambridge Brian Tom Jessica Barrett MRC Biostatistics Unit, University of Cambridge
Institute for Clarity in Documentation（清晰文档研究所）；Inria Paris-Rocquencourt（巴黎-罗克琴克研究所）；Rajiv Gandhi University（拉贾·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒尔研究实验室）；Northeastern University（东北大学）
SurvKAN: A Fully Parametric Survival Model Based on Kolmogorov-Arnold Networks
Department of Electronics, Information, and Bioengineering, Politecnico di Milano（电子信息生物工程系，米兰理工学院）；Department of Radiation Oncology, Mayo Clinic（放射肿瘤科，梅奥诊所）；Division of Radiation Oncology, IEO, European Institute of Oncology, IRCCS（放射肿瘤科，欧洲肿瘤研究所，IRCCS）；Department of Oncology and Hemato-Oncology, University of Milan（肿瘤学与血液肿瘤学系，米兰大学）
Deep Survival Analysis of Longitudinal EHR Data for Joint Prediction of Hospitalization and Death in COPD Patients
B2SLab, Institut de Recerca i Innovació en Salut (IRIS), Universitat Politècnica de Catalunya（B2SLab、创新与健康研究所（IRIS）、巴塞罗那理工大学）；Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)（生物医学研究网络（CIBER-BBN））；Institut de Recerca Sant Joan de Déu（圣约翰德德乌研究所）；Evidenze Health；Servei de Vigilància Epidemiològica i Resposta a Emergències de Salut Pública a Barcelona Sud, Subdirecció Regional a Barcelona, Secretaria de Salut Pública, Departament de Salut, Generalitat de Catalunya（巴塞罗那南部公共卫生流行病监测与应急响应服务，巴塞罗那地区副秘书处，公共卫生秘书处，加泰罗尼亚自治区卫生部门）；Fundació Docència i Recerca Mútua Terrassa, Catalonia, Spain（特拉维撒互惠教育与研究基金会，加泰罗尼亚，西班牙）
Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma
University Hospital Erlangen（埃尔朗根大学医院）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Comprehensive Cancer Center Erlangen-EMN（埃尔朗根-欧洲大都会区综合癌症中心）；The Bavarian Cancer Research Center（巴伐利亚癌症研究中心）；FAU Profile Center Immunomedicine（FAU免疫医学特色中心）
Prediction of Rapid Early Progression and Survival Risk with Pre-Radiation MRI in WHO Grade 4 Glioma Patients
Old Dominion University（奥多明尼昂大学）；OhioHealth Riverside Methodist Hospital（俄亥俄健康河景卫理公会医院）；OhioHealth Research Institute（俄亥俄健康研究所）；OhioHealth（俄亥俄健康）；The James Cancer Hospital and Solove Research Institute, Ohio State University Wexner Medical Center（俄亥俄州立大学韦克斯纳医学中心詹姆斯癌症医院及索洛夫研究所）
Deep learning cardiac motion analysis for human survival prediction
National Heart and Lung Institute, Imperial College London, UK（英国帝国理工学院国家心肺研究所）；Department of Computing, Imperial College London, UK（英国帝国理工学院计算机系）；Imperial College Healthcare NHS Trust, London, UK（英国伦敦帝国理工学院医疗NHS信托基金）；Division of Experimental Medicine, Department of Medicine, Imperial College London, UK（英国帝国理工学院医学系实验医学部）；National Heart Centre Singapore, Singapore, and Duke-NUS Graduate Medical School, Singapore（新加坡国家心脏中心和新加坡杜克-新加坡国立大学医学院）
TRAPS: Treatment-Assignment Prediction via Pathway-informed Stratification
Rajshahi University of Engineering \& Technology Bangladesh；University of Dhaka Bangladesh；American International University of Bangladesh Bangladesh；Sonoma State University United States；Iowa State University United States；Rajshahi University of Engineering \& Technology；University of Dhaka；American International University of Bangladesh；Sonoma State University；Iowa State University
Multimodal Integration of Longitudinal Noninvasive Diagnostics for Survival Prediction in Immunotherapy Using Deep Learning
Radboud University（拉德堡德大学）；The Netherlands Cancer Institute（荷兰癌症研究所）；Maastricht University（马斯特里赫特大学）；Oncode Institute（Oncode研究所）；Leiden University Medical Center（莱顿大学医学中心）；Centre Hospitalier Universitaire Vaudois（沃州大学医院）；University of Southern Denmark（南丹麦大学）
Neural interval-censored survival regression with feature selection
Instituto de Ciencias Matemáticas, CSIC（数学科学研究所（西班牙高等科学研究理事会））；Universidad Autónoma de Madrid（马德里自治大学）；Harvard University（哈佛大学）；Université Paris 1 Panthéon-Sorbonne（巴黎第一大学先贤祠-索邦大学）；University of North Carolina, Chapel Hill（北卡罗来纳大学教堂山分校）
Value-Monotonicity Matters: A Concordance Loss for Deep Survival Prediction
Department of Radiation Oncology, University of Colorado School of Medicine（科罗拉多大学医学院放射肿瘤学系）；Department of Radiation Oncology, University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心放射肿瘤学系）
