# Learning Contextual Hierarchical Structure of Medical Concepts with Poincair'e Embeddings to Clarify Phenotypes

arXiv: https://arxiv.org/abs/1811.01294 · HTML: https://mlsanity.com/paper/1811.01294/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2018.

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

- [HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering](https://mlsanity.com/paper/2604.21027/) (2026)
- [Risk Horizons: Structured Hypothesis Spaces for Longitudinal Clinical Prediction](https://mlsanity.com/paper/2602.12828/) (2026)

## Abstract (arXiv)

Biomedical association studies are increasingly done using clinical concepts, and in particular diagnostic codes from clinical data repositories as phenotypes. Clinical concepts can be represented in a meaningful, vector space using word embedding models. These embeddings allow for comparison between clinical concepts or for straightforward input to machine learning models. Using traditional approaches, good representations require high dimensionality, making downstream tasks such as visualization more difficult. We applied Poincar'e embeddings in a 2-dimensional hyperbolic space to a large-scale administrative claims database and show performance comparable to 100-dimensional embeddings in a euclidean space. We then examine disease relationships under different disease contexts to better understand potential phenotypes.