{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T06:04:07Z","timestamp":1775801047740,"version":"3.50.1"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T00:00:00Z","timestamp":1667952000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T00:00:00Z","timestamp":1667952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>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.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>This population-based\u00a0study involved patients aged\u2009\u2265\u200918\u00a0years 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\u00a0days 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\u00a0the testing set to evaluate the model performance.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Of the 372,293 patients with an index hospital hospitalization for pneumonia, 48,280 (12.97%) were readmitted within 30\u00a0days. Judged by AUROC in the testing data, rule-based model (0.6591) significantly outperformed decision tree (0.5783, <jats:italic>p<\/jats:italic> value &lt;\u20090.001), random forest (0.6509, <jats:italic>p<\/jats:italic> value\u2009&lt;\u20090.01) and LASSO (0.6087, <jats:italic>p<\/jats:italic> value\u2009&lt;\u20090.001), but was less superior than XGBoost (0.6606, <jats:italic>p<\/jats:italic> value\u2009=\u20090.015). 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.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>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.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-022-01995-3","type":"journal-article","created":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T08:03:54Z","timestamp":1667981034000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Machine learning methods to predict 30-day hospital readmission outcome among US adults with pneumonia: analysis of the national readmission database"],"prefix":"10.1186","volume":"22","author":[{"given":"Yinan","family":"Huang","sequence":"first","affiliation":[]},{"given":"Ashna","family":"Talwar","sequence":"additional","affiliation":[]},{"given":"Ying","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Rajender R.","family":"Aparasu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,9]]},"reference":[{"issue":"11","key":"1995_CR1","doi-asserted-by":"publisher","first-page":"1806","DOI":"10.1093\/cid\/cix647","volume":"65","author":"JA Ramirez","year":"2017","unstructured":"Ramirez JA, Wiemken TL, Peyrani P, et al. 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