# Interpretable machine learning for identifying ICU readmission risk in subgroups with probabilistic rules

## TL;DR

This paper introduces a machine learning model that identifies ICU readmission risks in patient subgroups using interpretable rules, helping clinicians make better discharge decisions.

## Contribution

The novel contribution is the application of the TURS model to ICU readmission prediction, enabling interpretable subgroup analysis with probabilistic rules.

## Key findings

- TURS identified subgroups with distinct feature distributions and importance, offering actionable insights for ICU discharge planning.
- The model achieved a ROC-AUC of 70.5% with a simple rule structure (5 rules, average length 2), outperforming other rule-based models.
- Subgroup analysis revealed unique feature impacts on readmission risk, highlighting patient heterogeneity.

## Abstract

Estimating readmission risk for intensive care unit (ICU) patients is critical for clinicians to optimize resource allocation and prevent premature discharges. Machine learning models currently applied to this task either lack interpretability or cannot identify patient subgroups with distinctive readmission risks and characteristics. We addressed this gap by introducing a cutting-edge rule-based model, namely truly unordered rule sets (TURS), to reveal heterogeneous readmission risks and subgroup-level patient characteristics.

We trained TURS on all ICU admissions from January 2011 to January 2020 at Leiden University Medical Center. For each subgroup, patient characteristics and the influence of feature variables on readmission risk were analyzed.

TURS identified subgroups with heterogeneous feature distributions and feature importance, providing actionable insights for ICU discharge planning. Its predictive performance (area under the receiver operating characteristic curve [ROC-AUC] 70.5%) and model complexity (5 rules, average length 2) surpassed other rule-based models.

Subgroup analysis highlighted the heterogeneity of patients. First, we compared the conditional probability distribution of each feature variable, conditioned on the fact that a patient was in a certain subgroup, with its unconditional distribution. We identified features deviating from the unconditional distribution, illustrating unique subgroup-specific implications. Furthermore, we demonstrated that features with the highest impact on the readmission risk were distinctive for each subgroup.

The TURS model provided a concise summary of patient subgroups, aiding ICU discharge decisions and advancing knowledge discovery in the ICU.