# Analysis of Risk Factor Domains in Psychosis Patient Health Records

arXiv: https://arxiv.org/abs/1809.05752 · HTML: https://mlsanity.com/paper/1809.05752/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2018.

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

- [From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?](https://mlsanity.com/paper/2403.11894/) (2024)
- [Aspect-Oriented Summarization for Psychiatric Short-Term Readmission Prediction](https://mlsanity.com/paper/2502.10388/) (2025)

## Abstract (arXiv)

Readmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problematic for psychiatric patients, so predicting which patients may be readmitted is critically important but also very difficult. Clinical narratives in psychiatric electronic health records (EHRs) span a wide range of topics and vocabulary; therefore, a psychiatric readmission prediction model must begin with a robust and interpretable topic extraction component. We created a data pipeline for using document vector similarity metrics to perform topic extraction on psychiatric EHR data in service of our long-term goal of creating a readmission risk classifier. We show initial results for our topic extraction model and identify additional features we will be incorporating in the future.