Machine learning methods to predict 30-day hospital readmission outcome among US adults with pneumonia: analysis of the national readmission database

Abstract

Background Hospital readmissions for pneumonia are a growing concern in the US, with significant consequences for costs and quality of care. This study developed the rule-based model and other machine learning (ML) models to predict 30-day readmission risk in patients with pneumonia and compared model performance. Methods This population-based study involved patients aged ≥ 18 years hospitalized with pneumonia from January 1, 2016, through November 30, 2016, using the Healthcare Cost and Utilization Project-National Readmission Database (HCUP-NRD). Rule-based algorithms and other ML algorithms, specifically decision trees, random forest, extreme gradient descent boosting (XGBoost), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to model all-cause readmissions 30 days post-discharge from index pneumonia hospitalization. A total of 61 clinically relevant variables were included for ML model development. Models were trained on randomly partitioned 50% of the data and evaluated using the remaining dataset. Model hyperparameters were tuned using the ten-fold cross-validation on the resampled training dataset. The area under the receiver operating curves (AUROC) and area under precision-recall curves (AUPRC) were calculated for the testing set to evaluate the model performance. Results Of the 372,293 patients with an index hospital hospitalization for pneumonia, 48,280 (12.97%) were readmitted within 30 days. Judged by AUROC in the testing data, rule-based model (0.6591) significantly outperformed decision tree (0.5783, p value < 0.001), random forest (0.6509, p value < 0.01) and LASSO (0.6087, p value < 0.001), but was less superior than XGBoost (0.6606, p value = 0.015). The AUPRC of the rule-based model in the testing data (0.2146) was higher than the decision tree (0.1560), random forest (0.2052), and LASSO (0.2042), but was similar to XGBoost (0.2147). The top risk-predictive rules captured by the rule-based algorithm were comorbidities, illness severity, disposition locations, payer type, age, and length of stay. These predictive risk factors were also identified by other ML models with high variable importance. Conclusion The performance of machine learning models for predicting readmission in pneumonia patients varied. The XGboost was better than the rule-based model based on the AUROC. However, important risk factors for predicting readmission remained consistent across ML models.

Sex‐based differences in left ventricular assist device clinical outcomes

“…We conducted a retrospective cohort study utilizing the Healthcare Cost and Utilization Project's (HCUP) Nationwide Readmission Database (NRD) from 2014 to 2020. The NRD includes nationally representative information on hospital readmissions for patients of all ages and all payers 6 . Both diagnoses and procedures were recognized according to their International Classification of Diseases Ninth Revision (ICD‐9) and Tenth Revision (ICD‐10) codes.…”

A Clinical Decision Support System for Edge/Cloud ICU Readmission Model Based on Particle Swarm Optimization, Ensemble Machine Learning, and Explainable Artificial Intelligence

“…The gradient-boosting ML mode performed the best since each DT is sequentially trained. In [27], Rule-based algorithms: DT, RF, XGBoost, and LASSO models were used using the 2016 Nationwide Readmissions Database (NRD) database from a US hospital. Data resampling techniques were used to provide more balanced data to effectively address the readmission and non-readmission groups.…”

“…In [14], the authors used Gradient-boosted, and AUC recorded 70. In [27], [28], the authors used XGboost and AUC and recorded 65.91 and 91, respectively. In [29], the authors used LR, and AUC recorded 70.6.…”

“…Datasets AUC [25] Ensemble model 11805 patients from Brazilian university hospital. AUC=91 [26] Ensemble model 3326 from MMIC AUC= 70.95 [14] Gradient-boosted machine 24885 from US hospital AUC=70 [27] XGboost from US hospital AUC=65.91 [28] XGBoost 5791 COVID-19 hospitalizations AUC=91 [29] LR 58036 patients from hospital in Asia. AUC= 70.6 [30] RF 1628 patients from hospital in Anhui AUC=87 [31] fuzzy modeling 26655 patients from MIMICII AUC= 72 [33] CNN-LSTM MMICII AUC=82.1 tients, the model may not be able to accurately predict their risk of readmission.…”

Predicting 7-day unplanned readmission in elderly patients with coronary heart disease using machine learning

“…A careful evaluation of the rehospitalization risk of elderly CHD patients plays a fundamental role in the clinical management of each patient. In recent years, the application of machine learning (ML) algorithms to predict clinical events has been actively conducted (9)(10)(11), and the development of a complicated and reliable classification tool has become possible. Therefore, we hypothesized that combining ML algorithms with patients' basic information might make it possible to produce reliable prediction models to predict the 7-day unplanned readmission of elderly CHD patients.…”