# CURENet: Combining Unified Representations for Efficient Chronic Disease Prediction

arXiv: https://arxiv.org/abs/2511.11423 · HTML: https://mlsanity.com/paper/2511.11423/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 76 references that are in the map.
- First posted on arXiv 14 Nov 2025.

## Builds on (most cited first, 30 of 37)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining](https://mlsanity.com/paper/2210.10341/) (2022)
- [A Transformer-based Framework for Multivariate Time Series Representation Learning](https://mlsanity.com/paper/2010.02803/) (2020)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism](https://mlsanity.com/paper/1608.05745/) (2016)
- [TabLLM: Few-shot Classification of Tabular Data with Large Language Models](https://mlsanity.com/paper/2210.10723/) (2022)
- [BEHRT: Transformer for Electronic Health Records](https://mlsanity.com/paper/1907.09538/) (2019)
- [Doctor AI: Predicting Clinical Events via Recurrent Neural Networks](https://mlsanity.com/paper/1511.05942/) (2015)
- [GRAM: Graph-based Attention Model for Healthcare Representation Learning](https://mlsanity.com/paper/1611.07012/) (2016)
- [Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks](https://mlsanity.com/paper/1706.05764/) (2017)
- [Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer](https://mlsanity.com/paper/1906.04716/) (2019)
- [RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records](https://mlsanity.com/paper/1805.10724/) (2018)
- [Hi-BEHRT: Hierarchical Transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records](https://mlsanity.com/paper/2106.11360/) (2021)
- [RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data](https://mlsanity.com/paper/1807.08820/) (2018)
- [Using Clinical Notes with Time Series Data for ICU Management](https://mlsanity.com/paper/1909.09702/) (2019)
- [BERT-XML: Large Scale Automated ICD Coding Using BERT Pretraining](https://mlsanity.com/paper/2006.03685/) (2020)
- [Large-scale Multi-label Text Classification - Revisiting Neural Networks](https://mlsanity.com/paper/1312.5419/) (2013)
- [Clinical XLNet: Modeling Sequential Clinical Notes and Predicting Prolonged Mechanical Ventilation](https://mlsanity.com/paper/1912.11975/) (2019)
- [Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare](https://mlsanity.com/paper/2105.07542/) (2021)
- [How to Leverage Multimodal EHR Data for Better Medical Predictions?](https://mlsanity.com/paper/2110.15763/) (2021)
- [Deep Short Text Classification with Knowledge Powered Attention](https://mlsanity.com/paper/1902.08050/) (2019)
- [TAPER: Time-Aware Patient EHR Representation](https://mlsanity.com/paper/1908.03971/) (2019)
- [Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs](https://mlsanity.com/paper/2112.05195/) (2021)
- [Evaluation Metrics for Unsupervised Learning Algorithms](https://mlsanity.com/paper/1905.05667/) (2019)

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

- [The Taxonomies, Training, and Applications of Event Stream Modelling for Electronic Health Records](https://mlsanity.com/paper/2603.14003/) (2026)

## Abstract (arXiv)

Electronic health records (EHRs) are designed to synthesize diverse data types, including unstructured clinical notes, structured lab tests, and time-series visit data. Physicians draw on these multimodal and temporal sources of EHR data to form a comprehensive view of a patient's health, which is crucial for informed therapeutic decision-making. Yet, most predictive models fail to fully capture the interactions, redundancies, and temporal patterns across multiple data modalities, often focusing on a single data type or overlooking these complexities. In this paper, we present CURENet, a multimodal model (Combining Unified Representations for Efficient chronic disease prediction) that integrates unstructured clinical notes, lab tests, and patients' time-series data by utilizing large language models (LLMs) for clinical text processing and textual lab tests, as well as transformer encoders for longitudinal sequential visits. CURENet has been capable of capturing the intricate interaction between different forms of clinical data and creating a more reliable predictive model for chronic illnesses. We evaluated CURENet using the public MIMIC-III and private FEMH datasets, where it achieved over 94% accuracy in predicting the top 10 chronic conditions in a multi-label framework. Our findings highlight the potential of multimodal EHR integration to enhance clinical decision-making and improve patient outcomes.