{"total":345,"rows":[{"id":"2301.03150","title":"MOTOR: A Time-To-Event Foundation Model For Structured Medical Records","created":"2023-01-09","cited_by_count":78,"area":null},{"id":"2305.14961","title":"Deep Learning for Survival Analysis: A Review","created":"2023-05-24","cited_by_count":75,"area":null},{"id":"2303.13948","title":"Knowledge Graphs: Opportunities and Challenges","created":"2023-03-24","cited_by_count":70,"area":null},{"id":"2103.09763","title":"Conformalized Survival Analysis","created":"2021-03-17","cited_by_count":53,"area":null},{"id":"2407.00224","title":"Multimodal Prototyping for cancer survival prediction","created":"2024-06-28","cited_by_count":47,"area":null},{"id":"2101.06536","title":"Deep Cox Mixtures for Survival Regression","created":"2021-01-16","cited_by_count":44,"area":null},{"id":"2110.00855","title":"SurvTRACE: Transformers for Survival Analysis with Competing Events","created":"2021-10-02","cited_by_count":41,"area":null},{"id":"2203.15588","title":"Deep Multi-modal Fusion of Image and Non-image Data in Disease Diagnosis and Prognosis: A Review","created":"2022-03-25","cited_by_count":37,"area":null},{"id":"2302.12692","title":"Language Models are Few-shot Learners for Prognostic Prediction","created":"2023-02-24","cited_by_count":37,"area":null},{"id":"2106.06468","title":"Locally Sparse Neural Networks for Tabular Biomedical Data","created":"2021-06-11","cited_by_count":34,"area":null},{"id":"2101.05346","title":"X-CAL: Explicit Calibration for Survival Analysis","created":"2021-01-13","cited_by_count":30,"area":null},{"id":"2202.12396","title":"Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications","created":"2022-02-24","cited_by_count":27,"area":null},{"id":"2106.05763","title":"A Deep Variational Approach to Clustering Survival Data","created":"2021-06-10","cited_by_count":25,"area":null},{"id":"2208.11080","title":"SurvSHAP(t): Time-dependent explanations of machine learning survival models","created":"2022-08-23","cited_by_count":24,"area":null},{"id":"2203.16024","title":"Longitudinal Fairness with Censorship","created":"2022-03-30","cited_by_count":22,"area":null},{"id":"2409.04290","title":"CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival Analysis","created":"2024-09-06","cited_by_count":22,"area":null},{"id":"2305.06703","title":"Neural Fine-Gray: Monotonic neural networks for competing risks","created":"2023-05-11","cited_by_count":20,"area":null},{"id":"2303.00865","title":"AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Images","created":"2023-03-01","cited_by_count":18,"area":null},{"id":"2204.07276","title":"auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data","created":"2022-04-15","cited_by_count":17,"area":null},{"id":"2104.08903","title":"SurvNAM: The machine learning survival model explanation","created":"2021-04-18","cited_by_count":16,"area":null},{"id":"2103.10928","title":"BERTSurv: BERT-Based Survival Models for Predicting Outcomes of Trauma Patients","created":"2021-03-19","cited_by_count":15,"area":null},{"id":"2211.10508","title":"Distributionally Robust Survival Analysis: A Novel Fairness Loss Without Demographics","created":"2022-11-18","cited_by_count":15,"area":null},{"id":"2301.12364","title":"Fair Decision-making Under Uncertainty","created":"2023-01-29","cited_by_count":15,"area":null},{"id":"2303.15699","title":"Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images","created":"2023-03-28","cited_by_count":15,"area":null},{"id":"2409.10538","title":"Fairness in Survival Analysis with Distributionally Robust Optimization","created":"2024-08-31","cited_by_count":14,"area":null},{"id":"2202.11089","title":"Counterfactual Phenotyping with Censored Time-to-Events","created":"2022-02-22","cited_by_count":13,"area":null},{"id":"2204.09633","title":"SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) prediction","created":"2022-04-20","cited_by_count":13,"area":null},{"id":"2404.08434","title":"An improved tabular data generator with VAE-GMM integration","created":"2024-04-12","cited_by_count":13,"area":null},{"id":"2102.02669","title":"OmiEmbed: a unified multi-task deep learning framework for multi-omics data","created":"2021-02-03","cited_by_count":12,"area":null},{"id":"2202.07423","title":"DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis","created":"2022-02-12","cited_by_count":12,"area":null},{"id":"2306.11912","title":"Copula-Based Deep Survival Models for Dependent Censoring","created":"2023-06-20","cited_by_count":12,"area":null},{"id":"2105.03875","title":"Bounding Information Leakage in Machine Learning","created":"2021-05-09","cited_by_count":11,"area":null},{"id":"2109.07711","title":"DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT","created":"2021-09-16","cited_by_count":11,"area":null},{"id":"2302.08015","title":"Individual Fairness under Uncertainty","created":"2023-02-16","cited_by_count":11,"area":null},{"id":"2304.04507","title":"hist2RNA: An efficient deep learning architecture to predict gene expression from breast cancer histopathology images","created":"2023-04-10","cited_by_count":10,"area":null},{"id":"2405.07374","title":"Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration","created":"2024-05-12","cited_by_count":10,"area":null},{"id":"2505.13072","title":"Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data","created":"2025-05-19","cited_by_count":10,"area":null},{"id":"2206.10477","title":"Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee","created":"2022-06-21","cited_by_count":9,"area":null},{"id":"2208.10759","title":"Survival Mixture Density Networks","created":"2022-08-23","cited_by_count":9,"area":null},{"id":"2310.18681","title":"DySurv: dynamic deep learning model for survival analysis with conditional variational inference","created":"2023-10-28","cited_by_count":9,"area":null},{"id":"2405.12963","title":"Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma","created":"2024-05-21","cited_by_count":9,"area":null},{"id":"2601.22259","title":"Tabular Foundation Models Can Do Survival Analysis","created":"2026-01-29","cited_by_count":9,"area":null},{"id":"2103.04556","title":"T-SCI: A Two-Stage Conformal Inference Algorithm with Guaranteed Coverage for Cox-MLP","created":"2021-03-08","cited_by_count":8,"area":null},{"id":"2111.08175","title":"Inverse-Weighted Survival Games","created":"2021-11-16","cited_by_count":8,"area":null},{"id":"2203.00144","title":"The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models","created":"2022-02-28","cited_by_count":8,"area":null},{"id":"2302.02807","title":"Federated Survival Forests","created":"2023-02-06","cited_by_count":8,"area":null},{"id":"2311.11659","title":"MGCT: Mutual-Guided Cross-Modality Transformer for Survival Outcome Prediction using Integrative Histopathology-Genomic Features","created":"2023-11-20","cited_by_count":8,"area":null},{"id":"2403.10581","title":"Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction","created":"2024-03-15","cited_by_count":8,"area":null},{"id":"2101.03170","title":"BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo values","created":"2021-01-07","cited_by_count":7,"area":null},{"id":"2201.03448","title":"BITES: Balanced Individual Treatment Effect for Survival data","created":"2022-01-05","cited_by_count":7,"area":null},{"id":"2202.03758","title":"Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data","created":"2022-02-08","cited_by_count":7,"area":null},{"id":"2204.01702","title":"Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI","created":"2022-04-01","cited_by_count":7,"area":null},{"id":"2207.02384","title":"Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data","created":"2022-07-06","cited_by_count":7,"area":null},{"id":"2207.05247","title":"FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysis","created":"2022-07-12","cited_by_count":7,"area":null},{"id":"2307.07513","title":"An empirical study of using radiology reports and images to improve ICU mortality prediction","created":"2023-06-20","cited_by_count":7,"area":null},{"id":"2411.09361","title":"Time-to-Event Pretraining for 3D Medical Imaging","created":"2024-11-14","cited_by_count":7,"area":null},{"id":"2506.02075","title":"Position: Stop Chasing the C-index when Evaluating Survival Analysis Models","created":"2025-06-02","cited_by_count":7,"area":null},{"id":"2102.01466","title":"Individual dynamic prediction of clinical endpoint from large dimensional longitudinal biomarker history: a landmark approach","created":"2021-02-02","cited_by_count":6,"area":null},{"id":"2106.09111","title":"An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data","created":"2021-06-16","cited_by_count":6,"area":null},{"id":"2110.08949","title":"Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards","created":"2021-10-17","cited_by_count":6,"area":null},{"id":"2111.03848","title":"Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival using a Full-Scale UNet with Attention","created":"2021-11-06","cited_by_count":6,"area":null},{"id":"2209.05485","title":"CustOmics: A versatile deep-learning based strategy for multi-omics integration","created":"2022-09-12","cited_by_count":6,"area":null},{"id":"2211.05409","title":"Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer","created":"2022-11-10","cited_by_count":6,"area":null},{"id":"2211.07814","title":"Extending the Neural Additive Model for Survival Analysis with EHR Data","created":"2022-11-15","cited_by_count":6,"area":null},{"id":"2303.10358","title":"Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions","created":"2023-03-18","cited_by_count":6,"area":null},{"id":"2308.02382","title":"Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomics","created":"2023-08-04","cited_by_count":6,"area":null},{"id":"2310.15472","title":"Interpretable Survival Analysis for Heart Failure Risk Prediction","created":"2023-10-24","cited_by_count":6,"area":null},{"id":"2405.07460","title":"HoneyBee: A Scalable Modular Framework for Creating Multimodal Oncology Datasets with Foundational Embedding Models","created":"2024-05-13","cited_by_count":6,"area":null},{"id":"2410.16765","title":"Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks","created":"2024-10-22","cited_by_count":6,"area":null},{"id":"2603.29475","title":"Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks","created":"2026-03-31","cited_by_count":6,"area":null},{"id":"2203.12204","title":"Interpretable Prediction of Lung Squamous Cell Carcinoma Recurrence With Self-supervised Learning","created":"2022-03-23","cited_by_count":5,"area":null},{"id":"2204.04542","title":"Survival Seq2Seq: A Survival Model based on Sequence to Sequence Architecture","created":"2022-04-09","cited_by_count":5,"area":null},{"id":"2205.01188","title":"Predicting Time-to-conversion for Dementia of Alzheimer's Type using Multi-modal Deep Survival Analysis","created":"2022-05-02","cited_by_count":5,"area":null},{"id":"2205.13481","title":"DeepJoint: Robust Survival Modelling Under Clinical Presence Shift","created":"2022-05-26","cited_by_count":5,"area":null},{"id":"2301.02383","title":"Deep Biological Pathway Informed Pathology-Genomic Multimodal Survival Prediction","created":"2023-01-06","cited_by_count":5,"area":null},{"id":"2301.12166","title":"Heterogeneous Datasets for Federated Survival Analysis Simulation","created":"2023-01-28","cited_by_count":5,"area":null},{"id":"2305.11575","title":"The Deep Promotion Time Cure Model","created":"2023-05-19","cited_by_count":5,"area":null},{"id":"2308.13104","title":"Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records","created":"2023-08-24","cited_by_count":5,"area":null},{"id":"2403.12818","title":"Dynamic Survival Analysis for Early Event Prediction","created":"2024-03-19","cited_by_count":5,"area":null},{"id":"2404.10761","title":"TorchSurv: A Lightweight Package for Deep Survival Analysis","created":"2024-04-16","cited_by_count":5,"area":null},{"id":"2406.04098","title":"A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data","created":"2024-06-06","cited_by_count":5,"area":null},{"id":"2410.20579","title":"Toward Conditional Distribution Calibration in Survival Prediction","created":"2024-10-27","cited_by_count":5,"area":null},{"id":"2412.01335","title":"A Versatile Influence Function for Data Attribution with Non-Decomposable Loss","created":"2024-12-02","cited_by_count":5,"area":null},{"id":"2505.11054","title":"NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification","created":"2025-05-16","cited_by_count":5,"area":null},{"id":"2107.12825","title":"Individual Survival Curves with Conditional Normalizing Flows","created":"2021-07-27","cited_by_count":4,"area":null},{"id":"2206.03256","title":"Flexible Group Fairness Metrics for Survival Analysis","created":"2022-05-26","cited_by_count":4,"area":null},{"id":"2207.05050","title":"A Federated Cox Model with Non-Proportional Hazards","created":"2022-07-11","cited_by_count":4,"area":null},{"id":"2208.05776","title":"Neural Networks for Scalar Input and Functional Output","created":"2022-08-10","cited_by_count":4,"area":null},{"id":"2302.10571","title":"SurvLIMEpy: A Python package implementing SurvLIME","created":"2023-02-21","cited_by_count":4,"area":null},{"id":"2303.05341","title":"Penalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patients","created":"2023-03-09","cited_by_count":4,"area":null},{"id":"2303.10789","title":"A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study","created":"2023-03-19","cited_by_count":4,"area":null},{"id":"2312.14651","title":"SAVAE: Leveraging the variational Bayes autoencoder for survival analysis","created":"2023-12-22","cited_by_count":4,"area":null},{"id":"2403.10250","title":"Interpretable Machine Learning for Survival Analysis","created":"2024-03-15","cited_by_count":4,"area":null},{"id":"2404.06421","title":"Efficient Training of Probabilistic Neural Networks for Survival Analysis","created":"2024-04-09","cited_by_count":4,"area":null},{"id":"2409.13363","title":"FPBoost: Fully Parametric Gradient Boosting for Survival Analysis","created":"2024-09-20","cited_by_count":4,"area":null},{"id":"2410.11340","title":"Toward a Well-Calibrated Discrimination via Survival Outcome-Aware Contrastive Learning","created":"2024-10-15","cited_by_count":4,"area":null},{"id":"2503.16645","title":"Ensemble Survival Analysis for Preclinical Cognitive Decline Prediction in Alzheimer's Disease Using Longitudinal Biomarkers","created":"2025-03-20","cited_by_count":4,"area":null},{"id":"2508.00657","title":"TrajSurv: Learning Continuous Latent Trajectories from Electronic Health Records for Trustworthy Survival Prediction","created":"2025-08-01","cited_by_count":4,"area":null},{"id":"2606.04564","title":"SurvPFN: Towards Foundation Models for Survival Predictions","created":"2026-06-03","cited_by_count":4,"area":null},{"id":"2105.01009","title":"Leveraging Deep Representations of Radiology Reports in Survival Analysis for Predicting Heart Failure Patient Mortality","created":"2021-05-03","cited_by_count":3,"area":null}],"next_cursor":"100"}