Observational data in medicine arise as a result of the complex interaction between patients and the healthcare system. The sampling process is often highly irregular and itself constitutes an informative process. When using such data to develop prediction models, this phenomenon is often ignored, leading to sub-optimal performance and generalisability of models when practices evolve. We propose a multi-task recurrent neural network which models three clinical presence dimensions -- namely the longitudinal, the inter-observation and the missingness processes -- in parallel to the survival outcome. On a prediction task using MIMIC III laboratory tests, explicit modelling of these three processes showed improved performance in comparison to state-of-the-art predictive models (C-index at 1 day horizon: 0.878). More importantly, the proposed approach was more robust to change in the clinical presence setting, demonstrated by performance comparison between patients admitted on weekdays and weekends. This analysis demonstrates the importance of studying and leveraging clinical presence to improve performance and create more transportable clinical models.
Predicting Breast Cancer Survival: A Survival Analysis Approach Using Log Odds and Clinical Variables
Federal College of Animal Health and Production Technology（联邦动物卫生与生产技术学院）；Universidad Mayor de San Andres（圣安德烈斯大学）；National University of Computer & Emerging Sciences-FAST（国立计算机与新兴科学大学（FAST大学））；University of Khartoum（喀土穆大学）；University of Jazan（贾赞大学）；New York Institute of Technology（纽约理工学院）；Khalifa Gull Nawaz Teaching Hospital（哈利法·古尔·纳瓦兹教学医院）
TrajSurv: Learning Continuous Latent Trajectories from Electronic Health Records for Trustworthy Survival Prediction
Department of Biomedical Informatics and Medical Education University of Washington（生物医学信息学与医学教育系，华盛顿大学）；Fred Hutch Cancer Center（Fred Hutch癌症中心）；Department of Biomedical Informatics Harvard University（生物医学信息学系，哈佛大学）
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
Attention-Based Synthetic Data Generation for Calibration-Enhanced Survival Analysis: A Case Study for Chronic Kidney Disease Using Electronic Health Records
Centre for Big Data Research in Health (CBDRH)（健康大数据研究中心）；The University of New South Wales（新南威尔士大学）
Tabular Foundation Models for Clinical Survival Analysis via Survival-Aware Adaptation
ADAPT Centre, Dublin City University（ADAPT中心，都柏林城市大学）；School of Computing, Dublin City University（都柏林城市大学计算机学院）；Department of Computer Science and Engineering, University of Bologna（博洛尼亚大学计算机科学与工程系）
Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features Relate
Heinz College of Information Systems and Public Policy, Carnegie Mellon University（卡内基梅隆大学信息系统与公共政策学院）；McKinsey & Company（麦肯锡公司）；Cornerstone Research（基石研究公司）；Microsoft Research（微软研究院）；National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）
Personalized Survival Predictions for Cardiac Transplantation via Trees of Predictors
University of California, Los Angeles（加州大学洛杉矶分校）；Nuffield College, Oxford University（牛津大学纳菲尔德学院）；Oxford Man Institute, Oxford University（牛津大学牛津曼研究所）；Alan Turing Institute（艾伦·图灵研究所）
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Duke University（杜克大学）；Singapore General Hospital（新加坡中央医院）；National University of Singapore（新加坡国立大学）；Singapore Health Services（新加坡保健服务集团）
COVID-Net Biochem: An Explainability-driven Framework to Building Machine Learning Models for Predicting Survival and Kidney Injury of COVID-19 Patients from Clinical and Biochemistry Data
Stony Brook University（石溪大学）；University of Waterloo（滑铁卢大学）；Waterloo Artificial Intelligence Institute（滑铁卢人工智能研究所）；DarwinAI Corp.（达尔文人工智能公司）；McGill University（麦吉尔大学）
Equitable Survival Prediction: A Fairness-Aware Survival Modeling (FASM) Approach
Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Duke University School of Medicine（杜克大学医学院）；Duke AI Health（杜克人工智能健康中心）；Mass General Brigham（麻省总医院布里格姆）；Harvard Medical School（哈佛医学院）；National University of Singapore（新加坡国立大学）
Comparison of the Cox proportional hazards model and Random Survival Forest algorithm for predicting patient-specific survival probabilities in clinical trial data
University of Augsburg（奥格斯堡大学）；University of Reading（雷丁大学）
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）
PISA: An AI Pipeline for Interpretable-by-design Survival Analysis Providing Multiple Complexity-Accuracy Trade-off Models
a Leiden University Medical Center, Leiden, The Netherlands；b Amsterdam University Medical Centers location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands；c Cancer Center Amsterdam, Cancer Treatment；Quality of Life, Amsterdam, The Netherlands；d Centrum Wiskunde \& Informatica, Amsterdam, The Netherlands
EAGLE: Efficient Alignment of Generalized Latent Embeddings for Multimodal Survival Prediction with Interpretable Attribution Analysis
Dept. of Machine Learning Moffitt Cancer Center（机器学习系莫菲特癌症中心）；Dept. of Cancer Epidemiology Moffitt Cancer Center（癌症流行病学系莫菲特癌症中心）；Dept. of Electrical Engineering University of South Florida（电气工程系佛罗里达州立大学）
Comparison of Machine Learning Classifiers to Predict Patient Survival and Genetics of GBM: Towards a Standardized Model for Clinical Implementation
Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）；Sant’Andrea Hospital, La Sapienza University（圣安德烈医院，罗马第一大学）；Bambino Gesù Children's Hospital, IRCCS（耶稣圣婴儿童医院，IRCCS）；Regina Elena National Cancer Institute, IRCCS（雷吉娜·埃琳娜国家癌症研究所，IRCCS）；Castelli Romani Hospital（卡斯特利罗马尼医院）
Toward Valid Generative Clinical Trial Data with Survival Endpoints
Inria（法国国家信息与自动化技术研究院）；Université Paris Cité（巴黎城市大学）；Inserm（法国国家卫生研究院）；HeKA；AP-HP（巴黎大学医院）；Hôpital Européen Georges Pompidou（乔治·蓬皮杜欧洲医院）；Unité de Recherche Clinique APHP（APHP临床研究中心）；CIC1418（临床研究1418中心）；Epidémiologie Clinique Paris France（巴黎流行病学临床研究中心）
Deep learning-based survival prediction for multiple cancer types using histopathology images
Google Health（谷歌健康）；Advanced Clinical；Tempus Labs Inc（Tempus实验室公司）
诊断时的预后信息对癌症治疗和监测具有重要意义。虽然癌症分期，组织病理学评估，分子特征和临床变量可以提供有用的预后见解，改善风险分层仍然是一个活跃的研究领域。我们开发了一个深度学习系统（DLS）来预测癌症基因组图谱（TCGA）中10种癌症类型的疾病特异性生存率。我们使用了一种没有像素级注释的弱监督方法，并测试了三种不同的生存损失函数。使用来自3，664例病例的9，086张载玻片开发了DLS，并使用来自1，216例病例的3，009张载玻片进行了评价。在包括所有10种癌症的组合队列的多变量Cox回归分析中，在校正癌症类型、分期、年龄和性别后，DLS与疾病特异性生存率显著相关（风险比为1.58，95%CI 1.28-1.70，p<0.0001）。在每种癌症调整的亚组分析中，DLS仍然是10种癌症中5种癌症的生存率的重要预测因素。与包括分期、年龄和性别的基线模型相比，模型的c指数在合并队列中显示出绝对3.7%的改善（95% CI 1.0-6.5）。此外，我们的模型在各个癌症阶段对患者进行了分层，特别是II期（p=0.025）和III期（p<0.001）。通过开发和评估多种癌症类型的预后模型，这项工作是探索使用深度学习和组织病理学图像直接预测临床结果的最全面的研究之一。我们的分析表明，这种方法有可能在多种癌症类型，甚至在特定的病理阶段提供预后信息。然而，鉴于临床事件的数量相对较少，我们观察到了…
