# Multi-Modal Perceiver Language Model for Outcome Prediction in Emergency Department

arXiv: https://arxiv.org/abs/2304.01233 · HTML: https://mlsanity.com/paper/2304.01233/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Placed in

- Multimodal LLMs / vision-language models / [Medical vision-language models](https://mlsanity.com/mllm/medical-vision-language-models/)

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Perceiver: General Perception with Iterative Attention](https://mlsanity.com/paper/2103.03206/) (2021)
- [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://mlsanity.com/paper/2202.03555/) (2022)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [Multimodal Learning with Transformers: A Survey](https://mlsanity.com/paper/2206.06488/) (2022)
- [A Systematic Literature Review of Automated ICD Coding and Classification Systems using Discharge Summaries](https://mlsanity.com/paper/2107.10652/) (2021)

## Built on by (most cited first, 1 of 1)

- [EQUITRIAGE: A Fairness Audit of Gender Bias in LLM-Based Emergency Department Triage](https://mlsanity.com/paper/2605.03998/) (2026)

## Abstract (arXiv)

Language modeling have shown impressive progress in generating compelling text with good accuracy and high semantic coherence. An interesting research direction is to augment these powerful models for specific applications using contextual information. In this work, we explore multi-modal language modeling for healthcare applications. We are interested in outcome prediction and patient triage in hospital emergency department based on text information in chief complaints and vital signs recorded at triage. We adapt Perceiver - a modality-agnostic transformer-based model that has shown promising results in several applications. Since vital-sign modality is represented in tabular format, we modified Perceiver position encoding to ensure permutation invariance. We evaluated the multi-modal language model for the task of diagnosis code prediction using MIMIC-IV ED dataset on 120K visits. In the experimental analysis, we show that mutli-modality improves the prediction performance compared with models trained solely on text or vital signs. We identified disease categories for which multi-modality leads to performance improvement and show that for these categories, vital signs have added predictive power. By analyzing the cross-attention layer, we show how multi-modality contributes to model predictions. This work gives interesting insights on the development of multi-modal language models for healthcare applications.