Contrastive Learning on Multimodal Analysis of Electronic Health Records
- URL: http://arxiv.org/abs/2403.14926v1
- Date: Fri, 22 Mar 2024 03:01:42 GMT
- Title: Contrastive Learning on Multimodal Analysis of Electronic Health Records
- Authors: Tianxi Cai, Feiqing Huang, Ryumei Nakada, Linjun Zhang, Doudou Zhou,
- Abstract summary: 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.
- Score: 15.392566551086782
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Electronic health record (EHR) systems contain a wealth of multimodal clinical data including structured data like clinical codes and unstructured data such as clinical notes. However, many existing EHR-focused studies has traditionally either concentrated on an individual modality or merged different modalities in a rather rudimentary fashion. This approach often results in the perception of structured and unstructured data as separate entities, neglecting the inherent synergy between them. Specifically, the two important modalities contain clinically relevant, inextricably linked and complementary health information. A more complete picture of a patient's medical history is captured by the joint analysis of the two modalities of data. Despite the great success of multimodal contrastive learning on vision-language, its potential remains under-explored in the realm of multimodal EHR, particularly in terms of its theoretical understanding. To accommodate the statistical analysis of multimodal EHR data, in this paper, we propose a novel multimodal 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 and connects the solution of the loss function to the singular value decomposition of a pointwise mutual information matrix. This connection paves the way for a privacy-preserving algorithm tailored for multimodal EHR feature representation learning. Simulation studies show that the proposed algorithm performs well under a variety of configurations. We further validate the clinical utility of the proposed algorithm in real-world EHR data.
Related papers
- Online Multi-modal Root Cause Analysis [61.94987309148539]
Root Cause Analysis (RCA) is essential for pinpointing the root causes of failures in microservice systems.
Existing online RCA methods handle only single-modal data overlooking, complex interactions in multi-modal systems.
We introduce OCEAN, a novel online multi-modal causal structure learning method for root cause localization.
arXiv Detail & Related papers (2024-10-13T21:47:36Z) - Advancing Multimodal Data Fusion in Pain Recognition: A Strategy Leveraging Statistical Correlation and Human-Centered Perspectives [0.3749861135832073]
This research presents a novel multimodal data fusion methodology for pain behavior recognition.
We introduce two key innovations: 1) integrating data-driven statistical relevance weights into the fusion strategy, and 2) incorporating human-centric movement characteristics into multimodal representation learning.
Our findings have significant implications for promoting patient-centered healthcare interventions and supporting explainable clinical decision-making.
arXiv Detail & Related papers (2024-03-30T11:13:18Z) - Joint Self-Supervised and Supervised Contrastive Learning for Multimodal
MRI Data: Towards Predicting Abnormal Neurodevelopment [5.771221868064265]
We present a novel joint self-supervised and supervised contrastive learning method to learn the robust latent feature representation from multimodal MRI data.
Our method has the capability to facilitate computer-aided diagnosis within clinical practice, harnessing the power of multimodal data.
arXiv Detail & Related papers (2023-12-22T21:05:51Z) - HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data [10.774128925670183]
This paper presents the Hybrid Early-fusion Attention Learning Network (HEALNet), a flexible multimodal fusion architecture.
We conduct multimodal survival analysis on Whole Slide Images and Multi-omic data on four cancer datasets from The Cancer Genome Atlas (TCGA)
HEALNet achieves state-of-the-art performance compared to other end-to-end trained fusion models.
arXiv Detail & Related papers (2023-11-15T17:06:26Z) - Quantifying & Modeling Multimodal Interactions: An Information
Decomposition Framework [89.8609061423685]
We propose an information-theoretic approach to quantify the degree of redundancy, uniqueness, and synergy relating input modalities with an output task.
To validate PID estimation, we conduct extensive experiments on both synthetic datasets where the PID is known and on large-scale multimodal benchmarks.
We demonstrate their usefulness in (1) quantifying interactions within multimodal datasets, (2) quantifying interactions captured by multimodal models, (3) principled approaches for model selection, and (4) three real-world case studies.
arXiv Detail & Related papers (2023-02-23T18:59:05Z) - Variational Distillation for Multi-View Learning [104.17551354374821]
We design several variational information bottlenecks to exploit two key characteristics for multi-view representation learning.
Under rigorously theoretical guarantee, our approach enables IB to grasp the intrinsic correlation between observations and semantic labels.
arXiv Detail & Related papers (2022-06-20T03:09:46Z) - Two heads are better than one: Enhancing medical representations by
pre-training over structured and unstructured electronic health records [23.379185792773875]
We propose a unified deep learning-based medical pre-trained language model, named UMM-PLM, to automatically learn representative features from multimodal EHRs.
We first developed parallel unimodal information representation modules to capture the unimodal-specific characteristic, where unimodal representations were learned from each data source separately.
A cross-modal module was further introduced to model the interactions between different modalities.
arXiv Detail & Related papers (2022-01-25T06:14:49Z) - Self-Supervised Multimodal Domino: in Search of Biomarkers for
Alzheimer's Disease [19.86082635340699]
We propose a taxonomy of all reasonable ways to organize self-supervised representation-learning algorithms.
We first evaluate models on toy multimodal MNIST datasets and then apply them to a multimodal neuroimaging dataset with Alzheimer's disease patients.
Results show that the proposed approach outperforms previous self-supervised encoder-decoder methods.
arXiv Detail & Related papers (2020-12-25T20:28:13Z) - Cross-Modal Information Maximization for Medical Imaging: CMIM [62.28852442561818]
In hospitals, data are siloed to specific information systems that make the same information available under different modalities.
This offers unique opportunities to obtain and use at train-time those multiple views of the same information that might not always be available at test-time.
We propose an innovative framework that makes the most of available data by learning good representations of a multi-modal input that are resilient to modality dropping at test-time.
arXiv Detail & Related papers (2020-10-20T20:05:35Z) - MS-Net: Multi-Site Network for Improving Prostate Segmentation with
Heterogeneous MRI Data [75.73881040581767]
We propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations.
Our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.
arXiv Detail & Related papers (2020-02-09T14:11:50Z) - Unpaired Multi-modal Segmentation via Knowledge Distillation [77.39798870702174]
We propose a novel learning scheme for unpaired cross-modality image segmentation.
In our method, we heavily reuse network parameters, by sharing all convolutional kernels across CT and MRI.
We have extensively validated our approach on two multi-class segmentation problems.
arXiv Detail & Related papers (2020-01-06T20:03:17Z)
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.