# Improving Clinical Outcome Predictions Using Convolution over Medical Entities with Multimodal Learning

arXiv: https://arxiv.org/abs/2011.12349 · HTML: https://mlsanity.com/paper/2011.12349/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

## Built on by (most cited first, 11 of 11)

- [Integrated multimodal artificial intelligence framework for healthcare applications](https://mlsanity.com/paper/2202.12998/) (2022)
- [Multimodal data matters: language model pre-training over structured and unstructured electronic health records](https://mlsanity.com/paper/2201.10113/) (2022)
- [Machine Learning for Multimodal Electronic Health Records-based Research: Challenges and Perspectives](https://mlsanity.com/paper/2111.04898/) (2021)
- [Multimodal Machine Learning in Precision Health](https://mlsanity.com/paper/2204.04777/) (2022)
- [Knowledge Graph Representations to enhance Intensive Care Time-Series Predictions](https://mlsanity.com/paper/2311.07180/) (2023)
- [Contrastive Learning on Multimodal Analysis of Electronic Health Records](https://mlsanity.com/paper/2403.14926/) (2024)
- [Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning](https://mlsanity.com/paper/2109.03062/) (2021)
- [Patient Outcome Predictions Improve Operations at a Large Hospital Network](https://mlsanity.com/paper/2305.15629/) (2023)
- [Using Clinical Drug Representations for Improving Mortality and Length of Stay Predictions](https://mlsanity.com/paper/2110.08918/) (2021)
- [CURENet: Combining Unified Representations for Efficient Chronic Disease Prediction](https://mlsanity.com/paper/2511.11423/) (2025)
- [On Preserving the Knowledge of Long Clinical Texts](https://mlsanity.com/paper/2311.01571/) (2023)

## Abstract (arXiv)

Early prediction of mortality and length of stay(LOS) of a patient is vital for saving a patient's life and management of hospital resources. Availability of electronic health records(EHR) makes a huge impact on the healthcare domain and there has seen several works on predicting clinical problems. However, many studies did not benefit from the clinical notes because of the sparse, and high dimensional nature. In this work, we extract medical entities from clinical notes and use them as additional features besides time-series features to improve our predictions. We propose a convolution based multimodal architecture, which not only learns effectively combining medical entities and time-series ICU signals of patients, but also allows us to compare the effect of different embedding techniques such as Word2vec, FastText on medical entities. In the experiments, our proposed method robustly outperforms all other baseline models including different multimodal architectures for all clinical tasks. The code for the proposed method is available at https://github.com/tanlab/ConvolutionMedicalNer.