Cardiac Complication Risk Profiling for Cancer Survivors via Multi-View Multi-Task Learning

• URL: http://arxiv.org/abs/2109.12276v1
• Date: Sat, 25 Sep 2021 05:00:55 GMT
• Title: Cardiac Complication Risk Profiling for Cancer Survivors via Multi-View Multi-Task Learning
• Authors: Thai-Hoang Pham, Changchang Yin, Laxmi Mehta, Xueru Zhang, Ping Zhang
• Abstract summary: Complication risk profiling is a key challenge in the healthcare domain due to the complex interaction between heterogeneous entities. We propose a multi-view multi-task network (MuViTaNet) for predicting the onset of multiple complications.
• Score: 11.13058781411915
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Complication risk profiling is a key challenge in the healthcare domain due to the complex interaction between heterogeneous entities (e.g., visit, disease, medication) in clinical data. With the availability of real-world clinical data such as electronic health records and insurance claims, many deep learning methods are proposed for complication risk profiling. However, these existing methods face two open challenges. First, data heterogeneity relates to those methods leveraging clinical data from a single view only while the data can be considered from multiple views (e.g., sequence of clinical visits, set of clinical features). Second, generalized prediction relates to most of those methods focusing on single-task learning, whereas each complication onset is predicted independently, leading to suboptimal models. We propose a multi-view multi-task network (MuViTaNet) for predicting the onset of multiple complications to tackle these issues. In particular, MuViTaNet complements patient representation by using a multi-view encoder to effectively extract information by considering clinical data as both sequences of clinical visits and sets of clinical features. In addition, it leverages additional information from both related labeled and unlabeled datasets to generate more generalized representations by using a new multi-task learning scheme for making more accurate predictions. The experimental results show that MuViTaNet outperforms existing methods for profiling the development of cardiac complications in breast cancer survivors. Furthermore, thanks to its multi-view multi-task architecture, MuViTaNet also provides an effective mechanism for interpreting its predictions in multiple perspectives, thereby helping clinicians discover the underlying mechanism triggering the onset and for making better clinical treatments in real-world scenarios.

Related papers

• A Learnable Multi-views Contrastive Framework with Reconstruction Discrepancy for Medical Time-Series [8.741139851597364]
  We propose incorporating external data from related tasks and leveraging AE-GAN to extract prior knowledge. We introduce LMCF, a framework that integrates a multi-head attention mechanism and adaptively learns representations from different views. Experiments on three target datasets demonstrate that our method consistently outperforms seven other baselines.
  arXiv  Detail & Related papers  (2025-01-30T14:20:11Z)
• Continually Evolved Multimodal Foundation Models for Cancer Prognosis [50.43145292874533]
  Cancer prognosis is a critical task that involves predicting patient outcomes and survival rates. Previous studies have integrated diverse data modalities, such as clinical notes, medical images, and genomic data, leveraging their complementary information. Existing approaches face two major limitations. First, they struggle to incorporate newly arrived data with varying distributions into training, such as patient records from different hospitals. Second, most multimodal integration methods rely on simplistic concatenation or task-specific pipelines, which fail to capture the complex interdependencies across modalities.
  arXiv  Detail & Related papers  (2025-01-30T06:49:57Z)
• ViKL: A Mammography Interpretation Framework via Multimodal Aggregation of Visual-knowledge-linguistic Features [54.37042005469384]
  We announce MVKL, the first multimodal mammography dataset encompassing multi-view images, detailed manifestations and reports. Based on this dataset, we focus on the challanging task of unsupervised pretraining. We propose ViKL, a framework that synergizes Visual, Knowledge, and Linguistic features.
  arXiv  Detail & Related papers  (2024-09-24T05:01:23Z)
• Single-Shared Network with Prior-Inspired Loss for Parameter-Efficient Multi-Modal Imaging Skin Lesion Classification [6.195015783344803]
  We introduce a multi-modal approach that efficiently integrates multi-scale clinical and dermoscopy features within a single network. Our method exhibits superiority in both accuracy and model parameters compared to currently advanced methods.
  arXiv  Detail & Related papers  (2024-03-28T08:00:14Z)
• Contrastive Learning on Multimodal Analysis of Electronic Health Records [15.392566551086782]
  We propose a novel feature embedding generative model and design a multimodal contrastive loss to obtain the multimodal EHR feature representation. Our theoretical analysis demonstrates the effectiveness of multimodal learning compared to single-modality learning. This connection paves the way for a privacy-preserving algorithm tailored for multimodal EHR feature representation learning.
  arXiv  Detail & Related papers  (2024-03-22T03:01:42Z)
• Multimodal Clinical Trial Outcome Prediction with Large Language Models [28.95412904299012]
  We propose a multimodal mixture-of-experts (LIFTED) approach for clinical trial outcome prediction. LIFTED unifies different modality data by transforming them into natural language descriptions. Then, LIFTED constructs unified noise-resilient encoders to extract information from modal-specific language descriptions.
  arXiv  Detail & Related papers  (2024-02-09T16:18:38Z)
• XAI for In-hospital Mortality Prediction via Multimodal ICU Data [57.73357047856416]
  We propose an efficient, explainable AI solution for predicting in-hospital mortality via multimodal ICU data. We employ multimodal learning in our framework, which can receive heterogeneous inputs from clinical data and make decisions. Our framework can be easily transferred to other clinical tasks, which facilitates the discovery of crucial factors in healthcare research.
  arXiv  Detail & Related papers  (2023-12-29T14:28:04Z)
• C^2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer network [67.97926983664676]
  We propose a cross-modal consistent multi-view medical report generation with a domain transfer network (C2M-DoT) C2M-DoT substantially outperforms state-of-the-art baselines in all metrics.
  arXiv  Detail & Related papers  (2023-10-09T02:31:36Z)
• A Transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics [63.106382317917344]
  We report a Transformer-based representation-learning model as a clinical diagnostic aid that processes multimodal input in a unified manner. The unified model outperformed an image-only model and non-unified multimodal diagnosis models in the identification of pulmonary diseases.
  arXiv  Detail & Related papers  (2023-06-01T16:23:47Z)
• 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)

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.