# Knowledge Graph Representations to enhance Intensive Care Time-Series Predictions

arXiv: https://arxiv.org/abs/2311.07180 · HTML: https://mlsanity.com/paper/2311.07180/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 13 Nov 2023.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [Graph-Guided Network for Irregularly Sampled Multivariate Time Series](https://mlsanity.com/paper/2110.05357/) (2021)
- [HiRID-ICU-Benchmark -- A Comprehensive Machine Learning Benchmark on High-resolution ICU Data](https://mlsanity.com/paper/2111.08536/) (2021)
- [Using Clinical Notes with Time Series Data for ICU Management](https://mlsanity.com/paper/1909.09702/) (2019)
- [Improving Clinical Outcome Predictions Using Convolution over Medical Entities with Multimodal Learning](https://mlsanity.com/paper/2011.12349/) (2020)
- [On the Importance of Clinical Notes in Multi-modal Learning for EHR Data](https://mlsanity.com/paper/2212.03044/) (2022)
- [Multi-modal Graph Learning over UMLS Knowledge Graphs](https://mlsanity.com/paper/2307.04461/) (2023)

## Built on by (most cited first, 3 of 3)

- [Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications](https://mlsanity.com/paper/2403.18316/) (2024)
- [Predictive AI with External Knowledge Infusion: Datasets and Benchmarks for Stock Markets](https://mlsanity.com/paper/2504.20058/) (2025)
- [KIRETT: Smart Integration of Vital Signs Data for Intelligent Decision Support in Rescue Scenarios](https://mlsanity.com/paper/2509.25923/) (2025)

## Abstract (arXiv)

Intensive Care Units (ICU) require comprehensive patient data integration for enhanced clinical outcome predictions, crucial for assessing patient conditions. Recent deep learning advances have utilized patient time series data, and fusion models have incorporated unstructured clinical reports, improving predictive performance. However, integrating established medical knowledge into these models has not yet been explored. The medical domain's data, rich in structural relationships, can be harnessed through knowledge graphs derived from clinical ontologies like the Unified Medical Language System (UMLS) for better predictions. Our proposed methodology integrates this knowledge with ICU data, improving clinical decision modeling. It combines graph representations with vital signs and clinical reports, enhancing performance, especially when data is missing. Additionally, our model includes an interpretability component to understand how knowledge graph nodes affect predictions.