ArticleCancers2023
Predicting Long-Term Care Service Demands for Cancer Patients: A Machine Learning Approach.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Functional Stratification and Long-Term Care Service Patterns among Discharged Patients with Cancer: A Retrospective Study from Taiwan.Journal of Cancer · 2026Article
- Exploring supportive care needs of lung cancer patients in China and predicting with machine learning models.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
- Understanding and alleviating informal caregiver burden through the development and validation of a caregiver strain index-based model in Taiwan.BMC geriatrics · 2024Article
- Estimating the indirect economic burden of cancer in Jordan: a retrospective observational study.BMJ open · 2024Observational
- Clinical Factors and Social Determinants of Health Predict Elder Mistreatment at Two Years Among Older Adults: A Preliminary Prediction Model.Inquiry : a journal of medical care organization, provision and financingObservational
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
backgroundLong-term care (LTC) service demands among cancer patients are significantly understudied, leading to gaps in healthcare resource allocation and policymaking.
objectiveThis study aimed to predict LTC service demands for cancer patients and identify the crucial factors.
methods3333 cases of cancers were included. We further developed two specialized prediction models: a Unified Prediction Model (UPM) and a Category-Specific Prediction Model (CSPM). The UPM offered generalized forecasts by treating all services as identical, while the CSPM built individual predictive models for each specific service type. Sensitivity analysis was also conducted to find optimal usage cutoff points for determining the usage and non-usage cases.
resultsService usage differences in lung, liver, brain, and pancreatic cancers were significant. For the UPM, the top 20 performance model cutoff points were adopted, such as through Logistic Regression (LR), Quadratic Discriminant Analysis (QDA), and XGBoost (XGB), achieving an AUROC range of 0.707 to 0.728. The CSPM demonstrated performance with an AUROC ranging from 0.777 to 0.837 for the top five most frequently used services. The most critical predictive factors were the types of cancer, patients' age and female caregivers, and specific health needs.
conclusionThe results of our study provide valuable information for healthcare decisions, resource allocation optimization, and personalized long-term care usage for cancer patients.
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