Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling

• URL: http://arxiv.org/abs/2210.12156v2
• Date: Mon, 5 Jun 2023 21:09:51 GMT
• Title: Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling
• Authors: Xinlu Zhang, Shiyang Li, Zhiyu Chen, Xifeng Yan, Linda Petzold
• Abstract summary: Health conditions among patients in intensive care units (ICUs) are monitored via electronic health records (EHRs) Dealing with irregularity in each single modality and integrating it into multimodal representations to improve medical predictions is a challenging problem. Our method first addresses irregularity in each single modality by dynamically incorporating hand-crafted imputation embeddings into learned embeddings via a gating mechanism. We observe relative improvements of 6.5%, 3.6%, and 4.3% in F1 for time series, clinical notes, and multimodal fusion, respectively.
• Score: 19.346610191591143
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Health conditions among patients in intensive care units (ICUs) are monitored via electronic health records (EHRs), composed of numerical time series and lengthy clinical note sequences, both taken at irregular time intervals. Dealing with such irregularity in every modality, and integrating irregularity into multimodal representations to improve medical predictions, is a challenging problem. Our method first addresses irregularity in each single modality by (1) modeling irregular time series by dynamically incorporating hand-crafted imputation embeddings into learned interpolation embeddings via a gating mechanism, and (2) casting a series of clinical note representations as multivariate irregular time series and tackling irregularity via a time attention mechanism. We further integrate irregularity in multimodal fusion with an interleaved attention mechanism across temporal steps. To the best of our knowledge, this is the first work to thoroughly model irregularity in multimodalities for improving medical predictions. Our proposed methods for two medical prediction tasks consistently outperforms state-of-the-art (SOTA) baselines in each single modality and multimodal fusion scenarios. Specifically, we observe relative improvements of 6.5\%, 3.6\%, and 4.3\% in F1 for time series, clinical notes, and multimodal fusion, respectively. These results demonstrate the effectiveness of our methods and the importance of considering irregularity in multimodal EHRs.

Related papers

• CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis [46.56667527672019]
  We introduce a Cross-Modal Temporal Pattern Discovery (CTPD) framework, designed to efficiently extract meaningful cross-modal temporal patterns from multimodal EHR data. Our approach introduces shared initial temporal pattern representations which are refined using slot attention to generate temporal semantic embeddings.
  arXiv  Detail & Related papers  (2024-11-01T15:54:07Z)
• PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation [51.509573838103854]
  We propose a semi-supervised learning framework, termed Progressive Mean Teachers (PMT), for medical image segmentation. Our PMT generates high-fidelity pseudo labels by learning robust and diverse features in the training process. Experimental results on two datasets with different modalities, i.e., CT and MRI, demonstrate that our method outperforms the state-of-the-art medical image segmentation approaches.
  arXiv  Detail & Related papers  (2024-09-08T15:02:25Z)
• DualTime: A Dual-Adapter Multimodal Language Model for Time Series Representation [25.47662257105448]
  We propose DualTime, a Dual-adapter multimodal language model for Time series representation. Our method outperforms state-of-the-art models in both supervised and unsupervised settings.
  arXiv  Detail & Related papers  (2024-06-07T14:34:28Z)
• Confidence-aware multi-modality learning for eye disease screening [58.861421804458395]
  We propose a novel multi-modality evidential fusion pipeline for eye disease screening. It provides a measure of confidence for each modality and elegantly integrates the multi-modality information. Experimental results on both public and internal datasets demonstrate that our model excels in robustness.
  arXiv  Detail & Related papers  (2024-05-28T13:27:30Z)
• Global Contrastive Training for Multimodal Electronic Health Records with Language Supervision [1.6245786035158123]
  This paper introduces a novel multimodal contrastive learning framework, specifically focusing on medical time series and clinical notes. The framework integrates temporal cross-attention transformers with a dynamic embedding and tokenization scheme for learning multimodal feature representations. Experiments with a real-world EHR dataset demonstrated that our framework outperformed state-of-the-art approaches on the exemplar task of predicting the occurrence of nine postoperative complications.
  arXiv  Detail & Related papers  (2024-04-10T04:19:59Z)
• Temporal Cross-Attention for Dynamic Embedding and Tokenization of Multimodal Electronic Health Records [1.6609516435725236]
  We introduce a dynamic embedding and tokenization framework for precise representation of multimodal clinical time series. Our framework outperformed baseline approaches on the task of predicting the occurrence of nine postoperative complications.
  arXiv  Detail & Related papers  (2024-03-06T19:46:44Z)
• TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspective [50.675845725806724]
  We propose a comprehensive framework of temporal counterfactual forecasting from an individual multiple treatment perspective (TCFimt) TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block to decouple a mixed treatment effect into separated main treatment effects and causal interactions. The proposed method shows satisfactory performance in predicting future outcomes with specific treatments and in choosing optimal treatment type and timing than state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-12-17T15:01:05Z)
• Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data [89.79617468457393]
  Training models with imbalance rate (class density discrepancy) may lead to suboptimal prediction. We propose a framework for training models for this imbalance issue. We demonstrate our model's improved performance in real-world medical datasets.
  arXiv  Detail & Related papers  (2022-07-23T00:39:53Z)
• Multi-view Integration Learning for Irregularly-sampled Clinical Time Series [1.9639092030562577]
  We propose a multi-view features integration learning from irregular time series data by self-attention mechanism in an imputation-free manner. We explicitly learn the relationships among the observed values, missing indicators, and time interval between the consecutive observations, simultaneously. We build an attention-based decoder as a missing value imputer that helps empower the representation learning of the inter-relations among multi-view observations.
  arXiv  Detail & Related papers  (2021-01-25T10:02:50Z)
• M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients [151.4352001822956]
  Early and accurate prediction of overall survival (OS) time can help to obtain better treatment planning for brain tumor patients. Existing prediction methods rely on radiomic features at the local lesion area of a magnetic resonance (MR) volume. We propose an end-to-end OS time prediction model; namely, Multi-modal Multi-channel Network (M2Net)
  arXiv  Detail & Related papers  (2020-06-01T05:21:37Z)

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.