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. 2023 Jun 5;23(1):104.
doi: 10.1186/s12911-023-02193-5.

Interpretable machine learning models for hospital readmission prediction: a two-step extracted regression tree approach

Xiaoquan Gao  1 , Sabriya Alam  2 , Pengyi Shi  3 , Franklin Dexter  4 , Nan Kong  5
Affiliations

Interpretable machine learning models for hospital readmission prediction: a two-step extracted regression tree approach

Xiaoquan Gao et al. BMC Med Inform Decis Mak. .

Abstract

Background: Advanced machine learning models have received wide attention in assisting medical decision making due to the greater accuracy they can achieve. However, their limited interpretability imposes barriers for practitioners to adopt them. Recent advancements in interpretable machine learning tools allow us to look inside the black box of advanced prediction methods to extract interpretable models while maintaining similar prediction accuracy, but few studies have investigated the specific hospital readmission prediction problem with this spirit.

Methods: Our goal is to develop a machine-learning (ML) algorithm that can predict 30- and 90- day hospital readmissions as accurately as black box algorithms while providing medically interpretable insights into readmission risk factors. Leveraging a state-of-art interpretable ML model, we use a two-step Extracted Regression Tree approach to achieve this goal. In the first step, we train a black box prediction algorithm. In the second step, we extract a regression tree from the output of the black box algorithm that allows direct interpretation of medically relevant risk factors. We use data from a large teaching hospital in Asia to learn the ML model and verify our two-step approach.

Results: The two-step method can obtain similar prediction performance as the best black box model, such as Neural Networks, measured by three metrics: accuracy, the Area Under the Curve (AUC) and the Area Under the Precision-Recall Curve (AUPRC), while maintaining interpretability. Further, to examine whether the prediction results match the known medical insights (i.e., the model is truly interpretable and produces reasonable results), we show that key readmission risk factors extracted by the two-step approach are consistent with those found in the medical literature.

Conclusions: The proposed two-step approach yields meaningful prediction results that are both accurate and interpretable. This study suggests a viable means to improve the trust of machine learning based models in clinical practice for predicting readmissions through the two-step approach.

Keywords: Administrative data; Hospital readmission; Interpretable machine learning; Risk factors; Risk prediction.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Distribution of the number of patients who are readmitted within 90 days of discharge
Fig. 2
Fig. 2
Regression tree extracted from sampled data (Four main specialties, 90-day readmission prediction)
Fig. 3
Fig. 3
Comparison of Features in Extracted Decision and Regression Trees from black box models on 90-day readmission rate. “General Class” in the table means patient with public insurance. The checkmark means the feature is identified as an influential feature from the corresponding machine learning models
See this image and copyright information in PMC

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