# Semantic Analysis of SNOMED CT Concept Co-occurrences in Clinical Documentation using MIMIC-IV

arXiv: https://arxiv.org/abs/2509.03662 · HTML: https://mlsanity.com/paper/2509.03662/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 3 Sep 2025.

## Builds on (most cited first, 9 of 9)

- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [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)
- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [Self-Alignment Pretraining for Biomedical Entity Representations](https://mlsanity.com/paper/2010.11784/) (2020)
- [GRAM: Graph-based Attention Model for Healthcare Representation Learning](https://mlsanity.com/paper/1611.07012/) (2016)
- [Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit](https://mlsanity.com/paper/2010.01165/) (2020)
- [Automated SNOMED CT Concept Annotation in Clinical Text Using Bi-GRU Neural Networks](https://mlsanity.com/paper/2508.02556/) (2025)

## Built on by (most cited first, 2 of 2)

- [An Intelligent Multi-task Supply Chain Model Based on Bio-inspired Networks](https://mlsanity.com/paper/2510.26203/) (2025)
- [SNOMED CT Concept Recommendation from Masked Clinical Context](https://mlsanity.com/paper/2609.17855/) (2026)

## Abstract (arXiv)

Clinical notes contain rich clinical narratives but their unstructured format poses challenges for large-scale analysis. Standardized terminologies such as SNOMED CT improve interoperability, yet understanding how concepts relate through co-occurrence and semantic similarity remains underexplored. In this study, we leverage the MIMIC-IV database to investigate the relationship between SNOMED CT concept co-occurrence patterns and embedding-based semantic similarity. Using Normalized Pointwise Mutual Information (NPMI) and pretrained embeddings (e.g., ClinicalBERT, BioBERT), we examine whether frequently co-occurring concepts are also semantically close, whether embeddings can suggest missing concepts, and how these relationships evolve temporally and across specialties. Our analyses reveal that while co-occurrence and semantic similarity are weakly correlated, embeddings capture clinically meaningful associations not always reflected in documentation frequency. Embedding-based suggestions frequently matched concepts later documented, supporting their utility for augmenting clinical annotations. Clustering of concept embeddings yielded coherent clinical themes (symptoms, labs, diagnoses, cardiovascular conditions) that map to patient phenotypes and care patterns. Finally, co-occurrence patterns linked to outcomes such as mortality and readmission demonstrate the practical utility of this approach. Collectively, our findings highlight the complementary value of co-occurrence statistics and semantic embeddings in improving documentation completeness, uncovering latent clinical relationships, and informing decision support and phenotyping applications.