Toward Predicting 30-Day Readmission Among Oncology Patients: Identifying Timely and Actionable Risk Factors
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Abstract
PURPOSE: Predicting 30-day readmission risk is paramount to improving the quality of patient care. In this study, we compare sets of patient-, provider-, and community-level variables that are available at two different points of a patient's inpatient encounter (first 48 hours and the full encounter) to train readmission prediction models and identify possible targets for appropriate interventions that can potentially reduce avoidable readmissions. METHODS: Using electronic health record data from a retrospective cohort of 2,460 oncology patients and a comprehensive machine learning analysis pipeline, we trained and tested models predicting 30-day readmission on the basis of data available within the first 48 hours of admission and from the entire hospital encounter. RESULTS: Leveraging all features, the light gradient boosting model produced higher, but comparable performance (area under receiver operating characteristic curve [AUROC]: 0.711) with the Epic model (AUROC: 0.697). Given features in the first 48 hours, the random forest model produces higher AUROC (0.684) than the Epic model (AUROC: 0.676). Both models flagged patients with a similar distribution of race and sex; however, our light gradient boosting and random forest models were more inclusive, flagging more patients among younger age groups. The Epic models were more sensitive to identifying patients with an average lower zip income. Our 48-hour models were powered by novel features at various levels: patient (weight change over 365 days, depression symptoms, laboratory values, and cancer type), hospital (winter discharge and hospital admission type), and community (zip income and marital status of partner). CONCLUSION: We developed and validated models comparable with the existing Epic 30-day readmission models with several novel actionable insights that could create service interventions deployed by the case management or discharge planning teams that may decrease readmission rates over time.
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Hwang 2023 is a validation study only including oncology patients, introducing significant bias with regard to patient selection.
“Hwang 2023 28 is a validation study only including oncology patients, introducing significant bias with regard to patient selection.”
From A Systematic Review and Meta-analysis of Externally Validated Epic Clinical Decision Support Tools (2026) · cites this paper as [28]
The Cohort and Dataset section describes a cohort restricted to oncology patients, supporting that part of the claim. But the Conclusions say the authors developed and validated models, so this was not a validation-only study. The Limitations section notes that the models were not evaluated at another hospital; it does not establish significant patient-selection bias. Our copy provides no page numbers for these passages. (LLM, conf=0.89)
Experimental — accuracy not yet measuredDrew 2023 had significant concerns with regard to patient selection because only patients receiving outpatient parenteral antibiotics were included.
“Similar to Hwang 2023, 28 Drew 2023 26 also had significant concerns with regard to patient selection; only patients receiving outpatient parenteral antibiotics were included.”
From A Systematic Review and Meta-analysis of Externally Validated Epic Clinical Decision Support Tools (2026) · cites this paper as [28]
Section 3.1 (page unavailable) describes a selected oncology cohort at one institute and excludes patients who moved away, died within 30 days, or were readmitted elsewhere. This supports the existence of patient-selection restrictions, but Hwang does not characterize them as significant concerns. The outpatient parenteral antibiotic restriction is attributed to Drew, not Hwang. (LLM, conf=0.78)
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · see citing works
<b>mice</b>: Multivariate Imputation by Chained Equations in<i>R</i>
Buuren, S. V., & Groothuis-Oudshoorn, K. (2011) · Journal of Statistical Software · doi:10.18637/jss.v045.i03 · Cited by 13,870 · see citing works
Peng, H., Long, F., & Ding, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.159 · Cited by 10,614 · see citing works
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
(2017) · HAL · Cited by 10,512 · see citing works
Larian Studios Dev Team (2024) · doi:10.1145/3292500.3330701 · Cited by 8,618 · cited 1× · see citing works
“First, an automated hyperparameter optimization sweep is conducted using the Optuna package 29 by further partitioning the training data into 3-fold training/testing sets and optimizing on balanced accuracy.”
Moons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147 · see citing works
Simulation physiologischer Regelkreise mit der objektorientierten Modellbibliothek “HumanLib”
Brunberg, A., Spillner, J. W., Autschbach, R., & Abel, D. (2011) · at - Automatisierungstechnik · doi:10.1524/auto.2011.0951 · Cited by 1,008 · see citing works
Artificial neural networks in medical diagnosis
Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl, A., & Havel, J. (2013) · Journal of Applied Biomedicine · doi:10.2478/v10136-012-0031-x · Cited by 898 · see citing works
Regularized gene selection in cancer microarray meta-analysis
Ma, S., & Huang, J. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-1 · Cited by 832 · see citing works
A random forest approach to the detection of epistatic interactions in case-control studies
Jiang, R., Tang, W., Wu, X., & Fu, W. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-s1-s65 · Cited by 306 · see citing works
Identification of Potentially Avoidable Hospitalizations in Patients With GI Cancer
Brooks, G. A., Abrams, T. A., Meyerhardt, J. A., Enzinger, P. C., Sommer, K. A., Dalby, C. K., Uno, H., Jacobson, J. O., Fuchs, C. S., & Schrag, D. (2014) · Journal of Clinical Oncology · doi:10.1200/jco.2013.52.4330 · Cited by 150 · see citing works
Cancer classification and prediction using logistic regression with Bayesian gene selection
Zhou, X., Liu, K., & Wong, S. T. C. (2004) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2004.07.009 · Cited by 134 · see citing works
Approaches to Regularized Regression – A Comparison between Gradient Boosting and the Lasso
Schmid, M. C., Gefeller, O., Waldmann, E., Mayr, A. J., & Hepp, T. (2016) · Methods of Information in Medicine · doi:10.3414/me16-01-0033 · Cited by 132 · see citing works
ExSTraCS 2.0: description and evaluation of a scalable learning classifier system
Urbanowicz, R. J., & Moore, J. H. (2015) · Evolutionary Intelligence · doi:10.1007/s12065-015-0128-8 · Cited by 128 · see citing works
Self-citationWhitney, R. L., Bell, J. F., Tancredi, D. J., Romano, P. S., Bold, R. J., & Joseph, J. G. (2017) · Journal of Clinical Oncology · doi:10.1200/jco.2017.72.4963 · Cited by 75 · see citing works
Montero, A. J., Stevenson, J. P., Guthrie, A. E., Best, C., Goodman, L. M., Shrotriya, S., Azzouqa, A., Parala‐Metz, A., Lagman, R. L., Bolwell, B. J., et al. (2016) · Journal of Oncology Practice · doi:10.1200/jop.2015.007880 · Cited by 63 · see citing works
Private naive bayes classification of personal biomedical data: Application in cancer data analysis
Wood, A., Shpilrain, V., Najarian, K., & Kahrobaei, D. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.11.018 · Cited by 59 · see citing works
Geriatric assessment as predictors of hospital readmission in older adults with cancer
Chiang, L., Liu, J., Flood, K. L., Carroll, M. B., Piccirillo, J. F., Stark, S. L., Wang, A. C., & Wildes, T. M. (2015) · Journal of Geriatric Oncology · doi:10.1016/j.jgo.2015.04.003 · Cited by 58 · see citing works
Manzano, J.-G. M., Gadiraju, S., Hiremath, A., Lin, H. Y., Farroni, J. S., & Halm, J. K. (2015) · Journal of Oncology Practice · doi:10.1200/jop.2014.003087 · Cited by 55 · see citing works
Using decision tree classification to assist in the prediction of Alzheimer's disease
AL-Dlaeen, D., & Alashqur, A. (2014) · doi:10.1109/csit.2014.6805989 · Cited by 53 · see citing works
Collective feature selection to identify crucial epistatic variants
Verma, S. S., Lucas, A., Zhang, X., Veturi, Y., DUDEK, S. M., Li, B., Li, R., Urbanowicz, R. J., Moore, J. H., Kim, D., et al. (2018) · BioData Mining · doi:10.1186/s13040-018-0168-6 · Cited by 34 · see citing works
Self-citationGallagher, D. M., Zhao, C., Brucker, A., Massengill, J., Kramer, P., Poon, E. G., & Goldstein, B. A. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10030103 · Cited by 28 · see citing works
Predictors of hospital readmission among older adults with cancer
Burhenn, P., Sun, C., Scher, K. S., Hsu, J., Pandya, P., Chui, C.-Y., Arsenyan, A., Mitani, D., Morrison, R. A., Katheria, V., et al. (2020) · Journal of Geriatric Oncology · doi:10.1016/j.jgo.2020.03.008 · Cited by 27 · see citing works
McKenna, D. R., Sullivan, M. A., Hill, J. M., Lowrey, C. H., Brown, J. R., Hickman, J., & Meehan, K. R. (2015) · The Journal of Community and Supportive Oncology · doi:10.12788/jcso.0168 · Cited by 19 · see citing works
Davoudi, A., Lee, N. S., Chivers, C., Delaney, T., Asch, E. L., Reitz, C., Mehta, S. J., Chaiyachati, K. H., & Mowery, D. L. (2020) · Journal of Medical Internet Research · doi:10.2196/22493 · Cited by 16 · see citing works
Self-citationEpstein, A. S., Crosbie, C., Martin, S. C., Egan, B. C., Goring, T. N., Koo, D. J., Kumar, C., Salvit, C., Capanu, M., Chou, J., et al. (2014) · Hospital Practice · doi:10.3810/hp.2014.12.1157 · Cited by 13 · see citing works
Wong, C. W., Chen, C., Rossi, L. A., Abila, M., Munu, J., Nakamura, R., & Eftekhari, Z. (2021) · JCO Clinical Cancer Informatics · doi:10.1200/cci.20.00127 · Cited by 12 · see citing works
Feature extraction for improving the support vector machine biomedical data classifier performance
Kostka, P., & Tkacz, E. (2008) · IEEE International Conference on Information Technology and Applications in Biomedicine · doi:10.1109/itab.2008.4570638 · Cited by 11 · see citing works
Evolutionary Feature Scaling in K-Nearest Neighbors Based on Label Dispersion Minimization
Basak, S., & Huber, M. (2020) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc42975.2020.9282834 · Cited by 9 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 2011;12:2825-2830.
- Urbanowicz RJ, Suri P, Lu Y, Moore JH, Ruth K, Stolzenberg-Solomon R, et al. A Rigorous Machine Learning Analysis Pipeline for Biomedical Binary Clas- sification: Application in Pancreatic Cancer Nested Case-control Studies with Implications for Bias Assessments. arXiv preprint arXiv:200812829. 2020;.
- Khorshid SF, Abdulazeez AM. breast cancer diagnosis based on k-nearest neighbors: A review. PalArch's Journal of Archaeology of Egypt/Egyptology. 2021;18(4):1927-1951.
- Akiba T, Sano S, Yanase T, Ohta T, Koyama M. Optuna: A next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining; 2019. p. 2623-2631.
Cited by
Sorted by citations
Li, C., Mowery, D. L., Ma, X., Yang, R., Vurgun, U., Hwang, S., Donnelly, H. K., Bandhey, H., Senathirajah, Y., Visweswaran, S., et al. (2024) · Journal of Clinical and Translational Science · doi:10.1017/cts.2024.571 · Cited by 54 · see citing works
Self-citationHuang, A. A., & Huang, S. Y. (2023) · Health Science Reports · doi:10.1002/hsr2.1635 · Cited by 23 · see citing works
Enhancing neuro-oncology care through equity-driven applications of artificial intelligence
Mehari, M., Sibih, Y., Dada, A., Chang, S. M., Wen, P. Y., Molinaro, A. M., Chukwueke, U. N., Budhu, J. A., Jackson, S., McFaline-Figueroa, J. R., et al. (2024) · Neuro-Oncology · doi:10.1093/neuonc/noae127 · Cited by 14 · see citing works
Le, N., Han, S., Kenawy, A. S., Kim, Y., & Park, C. (2025) · Cancer Control · doi:10.1177/10732748251332803 · Cited by 10 · see citing works
A Systematic Review and Meta-analysis of Externally Validated Epic Clinical Decision Support Tools
Patel, H., Crusco, S., Hansen, D., Morales, J., Patale, T., Chiuzan, C., Sands, M., Naidich, J., Myers, K., Davidson, K. W., et al. (2026) · Journal of General Internal Medicine · doi:10.1007/s11606-026-10381-y · Cited by 4 · see citing works
Li, C., Mowery, D. L., Ma, X., Yang, R., Vurgun, U., Hwang, S., Donnelly, H. K., Bandhey, H., Akhtar, Z., Senathirajah, Y., et al. (2024) · medRxiv · doi:10.1101/2024.02.04.24302242 · Cited by 4 · see citing works
Self-citationA Practical Approach for Targeting Structural Variants Genome-wide in Plasma Cell-free DNAPreprint
Murata, M. M., Igari, F., Urbanowicz, R. J., Mouakkad, L., Kim, S., Chen, Z., Vizio, D. D., Posadas, E. M., Giuliano, A. E., & Tanaka, H. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.25.564058 · Cited by 2 · see citing works
Self-citationMqadi, M., Mbunge, E., & Makaba, T. (2026) · Applied Sciences · doi:10.3390/app16052467 · Cited by 1 · see citing works
Sun, K. (2026) · JMIR Formative Research · doi:10.2196/87289 · Cited by 1 · see citing works
A Practical Approach for Targeting Structural Variants Genome-wide in Plasma Cell-free DNAPreprint
Tanaka, H., Murata, M. M., Igari, F., Urbanowicz, R. J., Mouakkad, L., Kim, S., Chen, Z., Vizio, D. D., Posadas, E. M., & Giuliano, A. E. (2024) · Research Square · doi:10.21203/rs.3.rs-3492157/v1 · Cited by 1 · see citing works
Self-citationLiu, S., Li, X., & Hu, Z. (2025) · medRxiv · doi:10.1101/2025.06.23.25330159 · Cited by 1 · see citing works
(2023) · Journal of Pain and Symptom Management · doi:10.1016/j.jpainsymman.2023.04.005
Evolutionary Machine Learning for Interpretable and eXplainable AI
Siddique, A., Browne, W. N., & Urbanowicz, R. J. (2024) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3638530.3658378
Self-citationLightfoot, M. D. S., Sinnott, J. A., Meade, C. E., Barrington, D. A. M., Cosgrove, C. M., Chambers, L., & Felix, A. S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5167757
Lightfoot, M. D. S., Sinnott, J. A., Meade, C. E., Barrington, D. A. M., Cosgrove, C. M., Chambers, L., & Felix, A. S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5167754
Lightfoot, M. D. S., Sinnott, J. A., Meade, C. E., Barrington, D. A. M., Cosgrove, C. M., Chambers, L., & Felix, A. S. (2025) · Gynecologic Oncology · doi:10.1016/j.ygyno.2025.07.021
Automated Machine Learning Tools for Data Science, Modeling, and Algorithm Benchmarking
Urbanowicz, R. J. (2025) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3712255.3716531
Self-citationTowards Extended Reality Intelligence for Monitoring and Predicting Patient Readmission Risks
Sanchez, M., Tran, N., & Chheang, V. (2026) · IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) · doi:10.1109/vrw70859.2026.00146
Chae, S., Harb, A., Youn, N., Annan, P. A. M., & Lopez, K. D. (2026) · Cancer Nursing · doi:10.1097/ncc.0000000000001606
Early Unplanned Readmissions and Mortality After Induction Chemotherapy in AML
Chae, S., Harb, A., Sutamtewagul, G., Perkhounkova, Y., Mueterthies, M., Hendricks, F., Hein, M., & Macieira, T. G. R. (2026) · JCO Clinical Cancer Informatics · doi:10.1200/cci-26-00018
Liu, H., Zhang, Y., Abedin, N., Kidd, N., Porter, K., Chen, C., You, W., & Li, J. (2026) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi69079.2026.00055
Introduction to Automated Machine Learning for Data Science, Modeling, and Benchmarking
Urbanowicz, R. J. (2026) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3795101.3807750
Self-citationStabellini, N., Favalesso, M. M., Szlejf, C., Miguel, C. G., Pestana, R. C., Kim, H., Ferreira, G. D. C., Waite, K. A., Montero, A. J., Barnholtz‐Sloan, J. S., et al. (2026) · Einstein (São Paulo) · doi:10.31744/einstein_journal/2026ao2210
Predicting Unplanned Admissions Following Chemotherapy: A Workload Planning Approach
Marples, D., Wong, D., & Zucker, K. (2026) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms69103.2026.00325
Zhou, G., Sha, Q., 赵丽红, Sooranna, S. R., Wang, Y., & Tan, H. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1929774
- Authors
- Hwang, S., Urbanowicz, R., Lynch, S., Vernon, T., Bresz, K., Giraldo, C., Kennedy, E., Leabhart, M., Bleacher, T., Ripchinski, M. R., Mowery, D. L., & Oyer, R. A.
- Affiliations
- University of Pennsylvania (US)
- Philadelphia College of Osteopathic Medicine (US)
- Abramson Cancer Center (US)
- Corresponding author
- Mowery, Danielle L.
- ORCID
- Venue
- JCO Clinical Cancer Informatics
- Published
- 2023-02
- Volume
- 7
- Issue
- 7
- Pages
- e2200097-e2200097
- ISSN
- 2473-4276 (ISSN-L)
- PMC
- PMC10476733
- OpenAlex
- W4321438200
- Cited by
- 25
- References
- 30
- Citations by year
- 2026: 11 · 2025: 6 · 2024: 5 · 2023: 3
- FWCI
- 2.36
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- JCO Clinical Cancer Informatics (publishedVersion)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (UL1TR001878)
- National Institute of Nursing Research (F31NR019919)
- National Center for Advancing Translational Sciences
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Emergency Medicine › Hospital Admissions and Outcomes
- Keywords
- 30-day readmission, readmission prediction, cancer patients, avoidable readmissions, LightGBM, random forest, depressive symptoms, weight change, discharge planning, case management
- MeSH terms
- Hospitalization, Humans, Neoplasms, Patient Readmission, Retrospective Studies, Risk Factors
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