Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features Relate

• URL: http://arxiv.org/abs/2007.07796v2
• Date: Wed, 5 Jun 2024 01:45:55 GMT
• Title: Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features Relate
• Authors: George H. Chen, Linhong Li, Ren Zuo, Amanda Coston, Jeremy C. Weiss,
• Abstract summary: We present a neural network framework for learning a survival model to predict a time-to-event outcome. In particular, we model each subject as a distribution over "topics" The presence of a topic in a subject means that specific clinical features are more likely to appear for the subject.
• Score: 10.709447977149532
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a neural network framework for learning a survival model to predict a time-to-event outcome while simultaneously learning a topic model that reveals feature relationships. In particular, we model each subject as a distribution over "topics", where a topic could, for instance, correspond to an age group, a disorder, or a disease. The presence of a topic in a subject means that specific clinical features are more likely to appear for the subject. Topics encode information about related features and are learned in a supervised manner to predict a time-to-event outcome. Our framework supports combining many different topic and survival models; training the resulting joint survival-topic model readily scales to large datasets using standard neural net optimizers with minibatch gradient descent. For example, a special case is to combine LDA with a Cox model, in which case a subject's distribution over topics serves as the input feature vector to the Cox model. We explain how to address practical implementation issues that arise when applying these neural survival-supervised topic models to clinical data, including how to visualize results to assist clinical interpretation. We study the effectiveness of our proposed framework on seven clinical datasets on predicting time until death as well as hospital ICU length of stay, where we find that neural survival-supervised topic models achieve competitive accuracy with existing approaches while yielding interpretable clinical topics that explain feature relationships. Our code is available at: https://github.com/georgehc/survival-topics

Related papers

• Molecular-driven Foundation Model for Oncologic Pathology [6.922502805825084]
  We introduce Threads, a slide-level foundation model capable of generating universal representations of whole-slide images of any size. Threads was pre-trained using a multimodal learning approach on a diverse cohort of 47,171 hematoxylin and eosin (H&E)-stained tissue sections.
  arXiv  Detail & Related papers  (2025-01-28T02:35:02Z)
• Neural decoding from stereotactic EEG: accounting for electrode variability across subjects [21.28778005847666]
  We introduce seegnificant: a training framework that can be used to decode behavior across subjects using sEEG data. We construct a multi-subject model trained on the combined data from 21 subjects performing a behavioral task.
  arXiv  Detail & Related papers  (2024-11-01T17:58:01Z)
• An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomes [5.257719744958367]
  Time-to-event outcomes have been studied extensively within the field of survival analysis. Monograph aims to provide a reasonably self-contained modern introduction to survival analysis.
  arXiv  Detail & Related papers  (2024-10-01T21:29:17Z)
• Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning [16.802013781690402]
  We propose a dynamic learning framework STHL, which introduces hypergraph to encode spatial-temporal data for sleep stage classification. Our proposed STHL outperforms the state-of-the-art models in sleep stage classification tasks.
  arXiv  Detail & Related papers  (2023-09-05T11:01:30Z)
• Neural Dynamic Focused Topic Model [2.9005223064604078]
  We leverage recent advances in neural variational inference and present an alternative neural approach to the dynamic Focused Topic Model. We develop a neural model for topic evolution which exploits sequences of Bernoulli random variables in order to track the appearances of topics.
  arXiv  Detail & Related papers  (2023-01-26T08:37:34Z)
• Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data [57.19441629270029]
  In this paper, we take advantage of the inherent properties of neural networks to federate the process of training of survival analysis models. In the realistic setting of small medical datasets and only a few data centers, this noise makes it harder for the models to converge. We propose DPFed-post which adds a post-processing stage to the private federated learning scheme.
  arXiv  Detail & Related papers  (2022-02-08T10:03:24Z)
• Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition [65.77962788209103]
  We propose class subset learning by dividing the long-tailed data into multiple class subsets according to prior knowledge. It enforces the model to focus on learning the subset-specific knowledge. The proposed framework proved to be effective for the long-tailed retinal diseases recognition task.
  arXiv  Detail & Related papers  (2021-04-22T13:39:33Z)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)
• Semi-Structured Deep Piecewise Exponential Models [2.7728956081909346]
  We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning. A proof of concept is provided by using the framework to predict Alzheimer's disease progression.
  arXiv  Detail & Related papers  (2020-11-11T14:41:19Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• Temporal Phenotyping using Deep Predictive Clustering of Disease Progression [97.88605060346455]
  We develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest. Experiments on two real-world datasets show that our model achieves superior clustering performance over state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2020-06-15T20:48:43Z)
• Modeling Shared Responses in Neuroimaging Studies through MultiView ICA [94.31804763196116]
  Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. We propose a novel MultiView Independent Component Analysis model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise. We demonstrate the usefulness of our approach first on fMRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects.
  arXiv  Detail & Related papers  (2020-06-11T17:29:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.