# Identifying Patterns of Associated-Conditions through Topic Models of Electronic Medical Records

arXiv: https://arxiv.org/abs/1711.10960 · HTML: https://mlsanity.com/paper/1711.10960/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2017.

## Built on by (most cited first, 5 of 5)

- [vONTSS: vMF based semi-supervised neural topic modeling with optimal transport](https://mlsanity.com/paper/2307.01226/) (2023)
- [DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM](https://mlsanity.com/paper/2310.15296/) (2023)
- [Natural Language Processing for Smart Healthcare](https://mlsanity.com/paper/2110.15803/) (2021)
- [KDSTM: Neural Semi-supervised Topic Modeling with Knowledge Distillation](https://mlsanity.com/paper/2307.01878/) (2023)
- [Co-occurrence of medical conditions: Exposing patterns through probabilistic topic modeling of SNOMED codes](https://mlsanity.com/paper/2109.09199/) (2021)

## Abstract (arXiv)

Multiple adverse health conditions co-occurring in a patient are typically associated with poor prognosis and increased office or hospital visits. Developing methods to identify patterns of co-occurring conditions can assist in diagnosis. Thus identifying patterns of associations among co-occurring conditions is of growing interest. In this paper, we report preliminary results from a data-driven study, in which we apply a machine learning method, namely, topic modeling, to electronic medical records, aiming to identify patterns of associated conditions. Specifically, we use the well established latent dirichlet allocation, a method based on the idea that documents can be modeled as a mixture of latent topics, where each topic is a distribution over words. In our study, we adapt the LDA model to identify latent topics in patients' EMRs. We evaluate the performance of our method both qualitatively, and show that the obtained topics indeed align well with distinct medical phenomena characterized by co-occurring conditions.