Multimodal Clinical Trial Outcome Prediction with Large Language Models

• URL: http://arxiv.org/abs/2402.06512v3
• Date: Thu, 9 May 2024 01:22:35 GMT
• Title: Multimodal Clinical Trial Outcome Prediction with Large Language Models
• Authors: Wenhao Zheng, Dongsheng Peng, Hongxia Xu, Yun Li, Hongtu Zhu, Tianfan Fu, Huaxiu Yao,
• Abstract summary: 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.
• Score: 30.201189349890267
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The clinical trial is a pivotal and costly process, often spanning multiple years and requiring substantial financial resources. Therefore, the development of clinical trial outcome prediction models aims to exclude drugs likely to fail and holds the potential for significant cost savings. Recent data-driven attempts leverage deep learning methods to integrate multimodal data for predicting clinical trial outcomes. However, these approaches rely on manually designed modal-specific encoders, which limits both the extensibility to adapt new modalities and the ability to discern similar information patterns across different modalities. To address these issues, we propose a multimodal mixture-of-experts (LIFTED) approach for clinical trial outcome prediction. Specifically, 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. Subsequently, a sparse Mixture-of-Experts framework is employed to further refine the representations, enabling LIFTED to identify similar information patterns across different modalities and extract more consistent representations from those patterns using the same expert model. Finally, a mixture-of-experts module is further employed to dynamically integrate different modality representations for prediction, which gives LIFTED the ability to automatically weigh different modalities and pay more attention to critical information. The experiments demonstrate that LIFTED significantly enhances performance in predicting clinical trial outcomes across all three phases compared to the best baseline, showcasing the effectiveness of our proposed key components.

Related papers

• Using Large Language Models for Expert Prior Elicitation in Predictive Modelling [53.54623137152208]
  This study proposes using large language models (LLMs) to elicit expert prior distributions for predictive models. We compare LLM-elicited and uninformative priors, evaluate whether LLMs truthfully generate parameter distributions, and propose a model selection strategy for in-context learning and prior elicitation. Our findings show that LLM-elicited prior parameter distributions significantly reduce predictive error compared to uninformative priors in low-data settings.
  arXiv  Detail & Related papers  (2024-11-26T10:13:39Z)
• Towards Precision Healthcare: Robust Fusion of Time Series and Image Data [8.579651833717763]
  We introduce a new method that uses two separate encoders, one for each type of data, allowing the model to understand complex patterns in both visual and time-based information. We also deal with imbalanced datasets and use an uncertainty loss function, yielding improved results. Our experiments show that our method is effective in improving multimodal deep learning for clinical applications.
  arXiv  Detail & Related papers  (2024-05-24T11:18:13Z)
• DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistency [18.291267748113142]
  We propose DrFuse to achieve effective clinical multi-modal fusion. We address the missing modality issue by disentangling the features shared across modalities and those unique within each modality. We validate the proposed method using real-world large-scale datasets, MIMIC-IV and MIMIC-CXR.
  arXiv  Detail & Related papers  (2024-03-10T12:41:34Z)
• Revealing Multimodal Contrastive Representation Learning through Latent Partial Causal Models [85.67870425656368]
  We introduce a unified causal model specifically designed for multimodal data. We show that multimodal contrastive representation learning excels at identifying latent coupled variables. Experiments demonstrate the robustness of our findings, even when the assumptions are violated.
  arXiv  Detail & Related papers  (2024-02-09T07:18:06Z)
• 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)
• Exploiting Modality-Specific Features For Multi-Modal Manipulation Detection And Grounding [54.49214267905562]
  We construct a transformer-based framework for multi-modal manipulation detection and grounding tasks. Our framework simultaneously explores modality-specific features while preserving the capability for multi-modal alignment. We propose an implicit manipulation query (IMQ) that adaptively aggregates global contextual cues within each modality.
  arXiv  Detail & Related papers  (2023-09-22T06:55:41Z)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data [42.56953523499849]
  We present PheME, an Ensemble framework using Multi-modality data of structured EHRs and unstructured clinical notes for accurate Phenotype prediction. We leverage ensemble learning to combine outputs from single-modal models and multi-modal models to improve phenotype predictions.
  arXiv  Detail & Related papers  (2023-03-19T23:41:04Z)
• Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability [82.29775890542967]
  Estimating personalized effects of treatments is a complex, yet pervasive problem. Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools. We use post-hoc feature importance methods to identify features that influence the model's predictions.
  arXiv  Detail & Related papers  (2022-06-16T17:59:05Z)
• Multi-objective optimization determines when, which and how to fuse deep networks: an application to predict COVID-19 outcomes [1.8351254916713304]
  We present a novel approach to optimize the setup of a multimodal end-to-end model. We test our method on the AIforCOVID dataset, attaining state-of-the-art results.
  arXiv  Detail & Related papers  (2022-04-07T23:07:33Z)

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.