# Identification, explanation and clinical evaluation of hospital patient subtypes

arXiv: https://arxiv.org/abs/2301.08019 · HTML: https://mlsanity.com/paper/2301.08019/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 19 Jan 2023.

## Builds on (most cited first, 3 of 3)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [bLIMEy: Surrogate Prediction Explanations Beyond LIME](https://mlsanity.com/paper/1910.13016/) (2019)
- [FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency](https://mlsanity.com/paper/1909.05167/) (2019)

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

- [Towards Personalised Patient Risk Prediction Using Temporal Hospital Data Trajectories](https://mlsanity.com/paper/2407.09373/) (2024)
- [Unsupervised Learning Approaches for Identifying ICU Patient Subgroups: Do Results Generalise?](https://mlsanity.com/paper/2403.02945/) (2024)

## Abstract (arXiv)

We present a pipeline in which unsupervised machine learning techniques are used to automatically identify subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. With the use of state-of-the-art explainability techniques, the identified subtypes are interpreted and assigned clinical meaning. In parallel, clinicians assessed intra-cluster similarities and inter-cluster differences of the identified patient subtypes within the context of their clinical knowledge. By confronting the outputs of both automatic and clinician-based explanations, we aim to highlight the mutual benefit of combining machine learning techniques with clinical expertise.