# Auditing ICU Readmission Rates in an Clinical Database: An Analysis of Risk Factors and Clinical Outcomes

arXiv: https://arxiv.org/abs/2304.05986 · HTML: https://mlsanity.com/paper/2304.05986/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 12 Apr 2023.

## Builds on (most cited first, 5 of 5)

- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [Aequitas: A Bias and Fairness Audit Toolkit](https://mlsanity.com/paper/1811.05577/) (2018)
- [Neural networks versus Logistic regression for 30 days all-cause readmission prediction](https://mlsanity.com/paper/1812.09549/) (2018)
- [A Machine Learning Model for Predicting, Diagnosing, and Mitigating Health Disparities in Hospital Readmission](https://mlsanity.com/paper/2206.06279/) (2022)

## Built on by (most cited first, 3 of 3)

- [Unmasking Bias in AI: A Systematic Review of Bias Detection and Mitigation Strategies in Electronic Health Record-based Models](https://mlsanity.com/paper/2310.19917/) (2023)
- [Navigating Fairness in Radiology AI: Concepts, Consequences,and Crucial Considerations](https://mlsanity.com/paper/2306.01333/) (2023)
- [Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions](https://mlsanity.com/paper/2403.19057/) (2024)

## Abstract (arXiv)

This study presents a machine learning (ML) pipeline for clinical data classification in the context of a 30-day readmission problem, along with a fairness audit on subgroups based on sensitive attributes. A range of ML models are used for classification and the fairness audit is conducted on the model predictions. The fairness audit uncovers disparities in equal opportunity, predictive parity, false positive rate parity, and false negative rate parity criteria on the MIMIC III dataset based on attributes such as gender, ethnicity, language, and insurance group. The results identify disparities in the model's performance across different groups and highlights the need for better fairness and bias mitigation strategies. The study suggests the need for collaborative efforts among researchers, policymakers, and practitioners to address bias and fairness in artificial intelligence (AI) systems.