# Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning

arXiv: https://arxiv.org/abs/1811.12276 · HTML: https://mlsanity.com/paper/1811.12276/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2018.

## Built on by (most cited first, 14 of 14)

- [Integrated multimodal artificial intelligence framework for healthcare applications](https://mlsanity.com/paper/2202.12998/) (2022)
- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Review of multimodal machine learning approaches in healthcare](https://mlsanity.com/paper/2402.02460/) (2024)
- [Literature-Augmented Clinical Outcome Prediction](https://mlsanity.com/paper/2111.08374/) (2021)
- [Multimodal data matters: language model pre-training over structured and unstructured electronic health records](https://mlsanity.com/paper/2201.10113/) (2022)
- [On the Importance of Clinical Notes in Multi-modal Learning for EHR Data](https://mlsanity.com/paper/2212.03044/) (2022)
- [FineEHR: Refine Clinical Note Representations to Improve Mortality Prediction](https://mlsanity.com/paper/2304.11794/) (2023)
- [Explainable AI for Clinical Outcome Prediction: A Survey of Clinician Perceptions and Preferences](https://mlsanity.com/paper/2502.20478/) (2025)
- [Improving Early Sepsis Prediction with Multi Modal Learning](https://mlsanity.com/paper/2107.11094/) (2021)
- [Patient Outcome Predictions Improve Operations at a Large Hospital Network](https://mlsanity.com/paper/2305.15629/) (2023)
- [Neural Entity Recognition with Gazetteer based Fusion](https://mlsanity.com/paper/2105.13225/) (2021)
- [Using Clinical Drug Representations for Improving Mortality and Length of Stay Predictions](https://mlsanity.com/paper/2110.08918/) (2021)
- [Multilingual Clinical NER for Diseases and Medications Recognition in Cardiology Texts using BERT Embeddings](https://mlsanity.com/paper/2510.17437/) (2025)
- [Inferring COVID-19 Biological Pathways from Clinical Phenotypes via Topological Analysis](https://mlsanity.com/paper/2101.07417/) (2021)

## Abstract (arXiv)

Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured clinical data. In this study, we explore how clinical text can complement a clinical predictive learning task. We leverage an internal medical natural language processing service to perform named entity extraction and negation detection on clinical notes and compose selected entities into a new text corpus to train document representations. We then propose a multimodal neural network to jointly train time series signals and unstructured clinical text representations to predict the in-hospital mortality risk for ICU patients. Our model outperforms the benchmark by 2% AUC.