ArticleJCO clinical cancer informatics2023
Thirty-Day Unplanned Hospital Readmissions in Patients With Cancer and the Impact of Social Determinants of Health: A Machine Learning Approach.
Article in JCO clinical cancer informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Machine Learning Techniques Used for the Identification of Sociodemographic Factors Associated With Cancer: Systematic Literature Review.Journal of medical Internet research · 2026Pooled it
- Review
- Clinical and Socioeconomic Predictors of 60-Day Rehospitalization After Oncologic Head and Neck Surgery.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026Article
- Externally Validated Machine Learning Models for 30-/90-/180-Day Unplanned Readmission After Head and Neck Cancer Hospitalizations in the United States.JCO clinical cancer informatics · 2026Article
- Predicting long-term hospital outcomes in older adults with the hospital frailty risk score: a nationwide study of repeated readmissions.BMC geriatrics · 2026Article
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- Cumulative frailty impact on unplanned hospitalization in older patients with cancer and excellent Eastern Cooperative Oncology Group performance.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Observational
- Impact of skilled nursing facility discharge on inpatient oncology quality outcomes.JNCI cancer spectrum · 2025Article
- Clinical Characteristics and Management of Patients Admitted to the Supportive Care Clinic and Predisposing Factors of Unplanned Hospital Readmission: Single-Center Experience.Journal of clinical medicine · 2025Article
- Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions.Biomedicines · 2025Review
- A novel machine learning-based cancer-specific cardiovascular disease risk score among patients with breast, colorectal, or lung cancer.JNCI cancer spectrum · 2025Article
- Unscheduled Hospital Admission as a Prognostic Factor in the Oncologic Patient: A Retrospective Study.Cureus · 2024Article
- Allostatic Load/Chronic Stress and Cardiovascular Outcomes in Patients Diagnosed With Breast, Lung, or Colorectal Cancer.Journal of the American Heart Association · 2024Article
- Socioeconomic Deprivation and Health Care Use in Patients Enrolled in SWOG Cancer Clinical Trials.JAMA network open · 2024Article
- Social Determinants of Health Data Improve the Prediction of Cardiac Outcomes in Females with Breast Cancer.Cancers · 2023Article
- Machine Learning-Based Prediction of Unplanned Readmission Due to Major Adverse Cardiac Events Among Hospitalized Patients with Blood Cancers.Cancer control : journal of the Moffitt Cancer CenterArticle
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeDevelop a cancer-specific machine learning (ML) model that accurately predicts 30-day unplanned readmissions in patients with solid tumors.
methodsThe initial cohort included patients 18 years or older diagnosed with a solid tumor. Two distinct cohorts were generated: one with and one without detailed social determinants of health (SDOHs) data. For each cohort, data were temporally partitioned in 70% (training), 20% (validation), and 10% (testing). Tree-based ML models were developed and validated on each cohort. The metrics used to evaluate the model's performance were receiver operating characteristic curve (ROC), area under the ROC curve, precision, recall (R), accuracy, and area under the precision-recall curve.
resultsWe included 13,717 patients in this study in two cohorts (5,059 without SDOH data and 8,658 with SDOH data). Unplanned 30-day readmission occurred in 21.3% of the cases overall. The five main non-SDOH factors most highly associated with an unplanned 30-day readmission (R, 0.74; IQR, 0.58-0.76) were: number of previous unplanned readmissions; higher Charlson comorbidity score; nonelective index admission; discharge to anywhere other than home, hospice, or nursing facility; and higher anion gap during the admission. Neighborhood crime index, neighborhood median home values, annual income, neighborhood median household income, and wealth index were the main five SDOH factors important for predicting a high risk for an unplanned hospital readmission (R, 0.66; IQR, 0.56-0.72). The models were not directly comparable.
conclusionKey drivers of unplanned readmissions in patients with cancer are complex and involve both clinical factors and SDOH. We developed a cancer-specific ML model that with reasonable accuracy identified patients with cancer at high risk for an unplanned hospital readmission.
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