Key result
XGBoost outperforms regression algorithms in predicting 30-day unplanned urology readmissions with an AUC of 0.81.
Why the study?
Unplanned hospital readmissions are serious medical adverse events, stressful to patients, and expensive, prompting the development and evaluation of a machine-learning probability calculator for 30-day readmissions after urology discharge.
Do machine learning classification algorithms improve the prediction of 30-day unplanned readmissions compared to regression algorithms in Urology patients?
Cohort (n=7,570)
No
Do machine learning classification algorithms improve the prediction of 30-day unplanned readmissions compared to regression algorithms in Urology patients?
Machine learning classification models, particularly XGBoost, demonstrate strong diagnostic performance for predicting 30-day unplanned readmissions in urology patients and outperform traditional regression models.
Answers are grounded in this paper and cite their sources.
May support ML for urology readmission risk stratification; leaves open prospective validation and outcome impact.
BACKGROUND: Unplanned hospital readmissions are serious medical adverse events, stressful to patients, and expensive for hospitals. This study aims to develop a probability calculator to predict unplanned readmissions (PURE) within 30-days after discharge from the department of Urology, and evaluate the respective diagnostic performance characteristics of the PURE probability calculator developed with machine learning (ML) algorithms comparing regression versus classification algorithms. METHODS: Eight ML models (i.e. logistic regression, LASSO regression, RIDGE regression, decision tree, bagged trees, boosted trees, XGBoost trees, RandomForest) were trained on 5.323 unique patients with 52 different features, and evaluated on diagnostic performance of PURE within 30 days of discharge from the department of Urology. RESULTS: Our main findings were that performances from classification to regression algorithms had good AUC scores (0.62-0.82), and classification algorithms showed a stronger overall performance as compared to models trained with regression algorithms. Tuning the best model, XGBoost, resulted in an accuracy of 0.83, sensitivity of 0.86, specificity of 0.57, AUC of 0.81, PPV of 0.95, and a NPV of 0.31. CONCLUSIONS: Classification models showed stronger performance than regression models with reliable prediction for patients with high probability of readmission, and should be considered as first choice. The tuned XGBoost model shows performance that indicates safe clinical appliance for discharge management in order to prevent an unplanned readmission at the department of Urology.
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Welvaars et al. (2023) conducted a cohort in Urology admission (n=7,570). XGBoost machine learning algorithm vs. Regression algorithms was evaluated on Prediction of 30-day unplanned hospital readmission (AUC 0.81). A tuned XGBoost classification model accurately predicted 30-day unplanned readmissions in urology patients with an AUC of 0.81, demonstrating stronger overall performance than regression-based algorithms.
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