# Phenotyping Clusters of Patient Trajectories suffering from Chronic Complex Disease

arXiv: https://arxiv.org/abs/2011.08356 · HTML: https://mlsanity.com/paper/2011.08356/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [Towards Personalised Patient Risk Prediction Using Temporal Hospital Data Trajectories](https://mlsanity.com/paper/2407.09373/) (2024)

## Abstract (arXiv)

Recent years have seen an increased focus into the tasks of predicting hospital inpatient risk of deterioration and trajectory evolution due to the availability of electronic patient data. A common approach to these problems involves clustering patients time-series information such as vital sign observations) to determine dissimilar subgroups of the patient population. Most clustering methods assume time-invariance of vital-signs and are unable to provide interpretability in clusters that is clinically relevant, for instance, event or outcome information. In this work, we evaluate three different clustering models on a large hospital dataset of vital-sign observations from patients suffering from Chronic Obstructive Pulmonary Disease. We further propose novel modifications to deal with unevenly sampled time-series data and unbalanced class distribution to improve phenotype separation. Lastly, we discuss further avenues of investigation for models to learn patient subgroups with distinct behaviour and phenotype.