Background: Long-term care (LTC) service demands among cancer patients are significantly understudied, leading to gaps in healthcare resource allocation and policymaking. Objective: This study aimed to predict LTC service demands for cancer patients and identify the crucial factors. Methods: 3333 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. Results: Service 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. Conclusion: The results of our study provide valuable information for healthcare decisions, resource allocation optimization, and personalized long-term care usage for cancer patients.
背景：癌症患者的长期护理服务需求研究严重不足，导致医疗资源配置和政策制定存在缺口。目的：本研究旨在预测癌症患者的长期护理服务需求并识别关键影响因素。方法：纳入3333例癌症病例。我们进一步开发了两种专业预测模型：统一预测模型和分类特异性预测模型。统一预测模型将所有服务视为同质进行整体预测，而分类特异性预测模型则为每类具体服务建立独立预测模型。通过敏感性分析确定服务使用与否的最佳界定阈值。结果：肺癌、肝癌、脑癌和胰腺癌的服务使用差异显著。统一预测模型采用逻辑回归、二次判别分析和XGBoost等前20位性能模型的阈值，获得0.707至0.728的AUROC值范围。分类特异性预测模型对前五位高频使用服务的预测性能表现优异，AUROC值介于0.777至0.837之间。最关键的影响因素包括癌症类型、患者年龄、女性照护者及特定健康需求。结论：本研究结果为医疗决策、资源配置优化及癌症患者个性化长期护理服务提供了重要参考依据。
Predicting Long-Term Care Service Demands for Cancer Patients: A Machine Learning Approach
肿瘤(癌症)患者之家